siRNA targeting glucagon receptor (GCGR)

ABSTRACT

Efficient sequence specific gene silencing is possible through the use of siRNA technology. By selecting particular siRNAs by rational design, one can maximize the generation of an effective gene silencing reagent, as well as methods for silencing genes. Methods, compositions, and kits generated through rational design of siRNAs are disclosed including those directed to nucleotide sequences for GCGR.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. Ser. No. 10/714,333, filed Nov. 14, 2003, which claims the benefit of U.S. Provisional Application No. 60/426,137, filed Nov. 14, 2002, and also claims the benefit of U.S. Provisional Application No. 60/502,050, filed Sep. 10, 2003; this application is also a continuation-in-part of U.S. Ser. No. 10/940,892, filed Sep. 14, 2004, which is a continuation of PCT Application No. PCT/US04/14885, international filing date May 12, 2004. The disclosures of the priority applications, including the sequence listings and tables submitted in electronic form in lieu of paper, are incorporated by reference into the instant specification.

SEQUENCE LISTING

The sequence listing for this application has been submitted in accordance with 37 CFR §1.52(e) and 37 CFR §1.821 on CD-ROM in lieu of paper on a disk containing the sequence listing file entitled “DHARMA_(—)2100-US90_CRF.txt” created Oct. 19, 2007, 104 kb. Applicants hereby incorporate by reference the sequence listing provided on CD-ROM in lieu of paper into the instant specification.

FIELD OF INVENTION

The present invention relates to RNA interference (“RNAi”).

BACKGROUND OF THE INVENTION

Relatively recently, researchers observed that double stranded RNA (“dsRNA”) could be used to inhibit protein expression. This ability to silence a gene has broad potential for treating human diseases, and many researchers and commercial entities are currently investing considerable resources in developing therapies based on this technology.

Double stranded RNA induced gene silencing can occur on at least three different levels: (i) transcription inactivation, which refers to RNA guided DNA or histone methylation; (ii) siRNA induced mRNA degradation; and (iii) mRNA induced transcriptional attenuation.

It is generally considered that the major mechanism of RNA induced silencing (RNA interference, or RNAi) in mammalian cells is mRNA degradation. Initial attempts to use RNAi in mammalian cells focused on the use of long strands of dsRNA. However, these attempts to induce RNAi met with limited success, due in part to the induction of the interferon response, which results in a general, as opposed to a target-specific, inhibition of protein synthesis. Thus, long dsRNA is not a viable option for RNAi in mammalian systems.

More recently it has been shown that when short (18-30 bp) RNA duplexes are introduced into mammalian cells in culture, sequence-specific inhibition of target mRNA can be realized without inducing an interferon response. Certain of these short dsRNAs, referred to as small inhibitory RNAs (“siRNAs”), can act catalytically at sub-molar concentrations to cleave greater than 95% of the target mRNA in the cell. A description of the mechanisms for siRNA activity, as well as some of its applications are described in Provost et al. (2002) Ribonuclease Activity and RNA Binding of Recombinant Human Dicer, EMBO J. 21(21): 5864-5874; Tabara et al. (2002) The dsRNA Binding Protein RDE-4 Interacts with RDE-1, DCR-1 and a DexH-box Helicase to Direct RNAi in C. elegans, Cell 109(7):861-71; Ketting et al. (2002) Dicer Functions in RNA Interference and in Synthesis of Small RNA Involved in Developmental Timing in C. elegans; Martinez et al., Single-Stranded Antisense siRNAs Guide Target RNA Cleavage in RNAi, Cell 110(5):563; Hutvagner & Zamore (2002) A microRNA in a multiple-turnover RNAi enzyme complex, Science 297:2056.

From a mechanistic perspective, introduction of long double stranded RNA into plants and invertebrate cells is broken down into siRNA by a Type II endonuclease known as Dicer. Sharp, RNA interference—2001, Genes Dev. 2001, 15:485. Dicer, a ribonuclease-III-like enzyme, processes the dsRNA into 19-23 base pair short interfering RNAs with characteristic two base 3′ overhangs. Bernstein, Caudy, Hammond, & Hannon (2001) Role for a bidentate ribonuclease in the initiation step of RNA interference, Nature 409:363. The siRNAs are then incorporated into an RNA-induced silencing complex (RISC) where one or more helicases unwind the siRNA duplex, enabling the complementary antisense strand to guide target recognition. Nykanen, Haley, & Zamore (2001) ATP requirements and small interfering RNA structure in the RNA interference pathway, Cell 107:309. Upon binding to the appropriate target mRNA, one or more endonucleases within the RISC cleaves the target to induce silencing. Elbashir, Lendeckel, & Tuschl (2001) RNA interference is mediated by 21- and 22-nucleotide RNAs, Genes Dev. 15:188, FIG. 1.

The interference effect can be long lasting and may be detectable after many cell divisions. Moreover, RNAi exhibits sequence specificity. Kisielow, M. et al., (2002) Isoform-specific knockdown and expression of adaptor protein ShcA using small interfering RNA, J. Biochem. 363: 1-5. Thus, the RNAi machinery can specifically knock down one type of transcript, while not affecting closely related mRNA. These properties make siRNA a potentially valuable tool for inhibiting gene expression and studying gene function and drug target validation. Moreover, siRNAs are potentially useful as therapeutic agents against: (1) diseases that are caused by over-expression or misexpression of genes; and (2) diseases brought about by expression of genes that contain mutations.

Successful siRNA-dependent gene silencing depends on a number of factors. One of the most contentious issues in RNAi is the question of the necessity of siRNA design, i.e., considering the sequence of the siRNA used. Early work in C. elegans and plants circumvented the issue of design by introducing long dsRNA (see, for instance, Fire, A. et al. (1998) Nature 391:806-811). In this primitive organism, long dsRNA molecules are cleaved into siRNA by Dicer, thus generating a diverse population of duplexes that can potentially cover the entire transcript. While some fraction of these molecules are non-functional (i.e., induce little or no silencing) one or more have the potential to be highly functional, thereby silencing the gene of interest and alleviating the need for siRNA design. Unfortunately, due to the interferon response, this same approach is unavailable for mammalian systems. While this effect can be circumvented by bypassing the Dicer cleavage step and directly introducing siRNA, this tactic carries with it the risk that the chosen siRNA sequence may be non-functional or semi-functional.

A number of researches have expressed the view that siRNA design is not a crucial element of RNAi. On the other hand, others in the field have begun to explore the possibility that RNAi can be made more efficient by paying attention to the design of the siRNA. Unfortunately, none of the reported methods have provided a satisfactory scheme for reliably selecting siRNA with acceptable levels of functionality. Accordingly, there is a need to develop rational criteria by which to select siRNA with an acceptable level of functionality, and to identify siRNA that have this improved level of functionality, as well as to identify siRNAs that are hyperfunctional.

SUMMARY OF THE INVENTION

The present invention is directed to increasing the efficiency of RNAi, particularly in mammalian systems. Accordingly, the present invention provides kits, siRNAs and methods for increasing siRNA efficacy.

According to a first embodiment, the present invention provides a kit for gene silencing, wherein said kit is comprised of a pool of at least two siRNA duplexes, each of which is comprised of a sequence that is complementary to a portion of the sequence of one or more target messenger RNA, and each of which is selected using non-target specific criteria.

According to a second embodiment, the present invention provides a method for selecting an siRNA, said method comprising applying selection criteria to a set of potential siRNA that comprise 18-30 base pairs, wherein said selection criteria are non-target specific criteria, and said set comprises at least two siRNAs and each of said at least two siRNAs contains a sequence that is at least substantially complementary to a target gene; and determining the relative functionality of the at least two siRNAs.

According to a third embodiment, the present invention also provides a method for selecting an siRNA wherein said selection criteria are embodied in a formula comprising: (−14)*G₁₃−13*A₁−12*U₇−11*U₂−10*A₁₁−10*U₄−10*C₃−10*C₅−10*C₆−9*A₁₀− 9*U₉−9*C₁₈−8*G₁₀−7*U₁−7*U₁₆−7*C₁₇−7*C₁₉+7*U₁₇+8*A₂+8*A₄+8*A₅+8*C₄ +9*G₈+10*A₇+10*U₁₈+11*A₁₉+11*C₉+15*G₁+18*A₃+19*U₁₀−Tm−3*(GC_(total)) −6*(GC₁₅₋₁₉)−30*X; or  Formula VIII (−8)*A1+(−1)*A2+(12)*A3+(7)*A4+(18)*A5+(12)*A6+ (19)*A7+(6)*A8+(−4)*A9+(−5)*A10+(−2)*A11+(−5)*A12+(17)*A13+(− 3)*A14+(4)*A15+(2)*A16+(8)*A17+(11)*A18+(30)*A19+(−13)*U1+(− 10)*U2+(2)*U3+(−2)*U4+(−5)*U5+(5)*U6+(−2)*U7+(−10)*U8+(− 5)*U9+(15)*U10+(−1)*U11+(0)*U12+(10)*U13+(−9)*U14+(−13)*U15+(− 10)*U16+(3)*U17+(9)*U18+(9)*U19+(7)*C1+(3)*C2+(−21)*C3+(5)*C4+(− 9)*C5+(−20)*C6+(−18)*C7+(−5)*C8+(5)*C9+(1)*C10+(2)*C11+(− 5)*C12+(−3)*C13+(−6)*C14+(−2)*C15+(−5)*C16+(−3)*C17+(−12)*C18+(− 18)*C19+(14)*G1+(8)*G2+(7)*G3+(−10)*G4+(− 4)*G5+(2)*G6+(1)*G7+(9)*G8+(5)*G9+(−11)*G10+(1)*G11+(9)*G12+(− 24)*G13+(18)*G14+(11)*G15+(13)*G16+(−7)*G17+(−9)*G18+(−22)*G19+ 6*(number of A+U in position 15-19)−3*(number of G+C in whole siRNA),  Formula X wherein position numbering begins at the 5′-most position of a sense strand, and A₁=1 if A is the base at position 1 of the sense strand, otherwise its value: is 0; A₂=1 if A is the base at position 2 of the sense strand, otherwise its value: is 0; A₃=1 if A is the base at position 3 of the sense strand, otherwise its value: is 0; A₄=1 if A is the base at position 4 of the sense strand, otherwise its value is 0; A₅=1 if A is the base at position 5 of the sense strand, otherwise its value is 0; A₆=1 if A is the base at position 6 of the sense strand, otherwise its value is 0; A₇=1 if A is the base at position 7 of the sense strand, otherwise its value is 0; A₁₀=1 if A is the base at position 10 of the sense strand, otherwise its value is 0; A₁₁=1 if A is the base at position 11 of the sense strand, otherwise its value is 0; A₁₃=1 if A is the base at position 13 of the sense strand, otherwise its value is 0; A₁₉=1 if A is the base at position 19 of the sense strand, otherwise if another base is present or the sense strand is only 18 base pairs in length, its value is 0; C₃=1 if C is the base at position 3 of the sense strand, otherwise its value is 0; C₄=1 if C is the base at position 4 of the sense strand, otherwise its value is 0; C₅=1 if C is the base at position 5 of the sense strand, otherwise its value is 0; C₆=1 if C is the base at position 6 of the sense strand, otherwise its value is 0; C₇=1 if C is the base at position 7 of the sense strand, otherwise its value: is 0; C₉=1 if C is the base at position 9 of the sense strand, otherwise its value is 0; C₁₇=1 if C is the base at position 17 of the sense strand, otherwise its value is 0; C₁₈=1 if C is the base at position 18 of the sense strand, otherwise its value is 0; C₁₉=1 if C is the base at position 19 of the sense strand, otherwise if another base is present or the sense strand is only 18 base pairs in length, its value is 0; G₁=1 if G is the base at position 1 on the sense strand, otherwise its value is 0; G₂=1 if G is the base at position 2 of the sense strand, otherwise its value is 0; G₈=1 if G is the base at position 8 on the sense strand, otherwise its value is 0; G₁₀=1 if G is the base at position 10 on the sense strand, otherwise its value is 0; G₁₃=1 if G is the base at position 13 on the sense strand, otherwise its value is 0; G₁₉=1 if G is the base at position 19 of the sense strand, otherwise if another base is present or the sense strand is only 18 base pairs in length, its value is 0; U₁=1 if U is the base at position 1 on the sense strand, otherwise its value is 0; U₂=1 if U is the base at position 2 on the sense strand, otherwise its value is 0; U₃=1 if U is the base at position 3 on the sense strand, otherwise its value is 0; U₄=1 if U is the base at position 4 on the sense strand, otherwise its value is 0; U₇=1 if U is the base at position 7 on the sense strand, otherwise its value is 0; U₉=1 if U is the base at position 9 on the sense strand, otherwise its value is 0; U₁₀=1 if U is the base at position 10 on the sense strand, otherwise its value is 0; U₁₅=1 if U is the base at position 15 on the sense strand, otherwise its value is 0; U₁₆=1 if U is the base at position 16 on the sense strand, otherwise its value is 0; U₁₇=1 if U is the base at position 17 on the sense strand, otherwise its value is 0; U₁₈=1 if U is the base at position 18 on the sense strand, otherwise its value is 0. GC₁₅₋₁₉=the number of G and C bases within positions 15-19 of the sense strand, or within positions 15-18 if the sense strand is only 18 base pairs in length; GC_(total)=the number of G and C bases in the sense strand; Tm=100 if the siRNA oligo has the internal repeat longer then 4 base pairs, otherwise its value is 0; and X=the number of times that the same nucleotide repeats four or more times in a row.

According to a fourth embodiment, the invention provides a method for developing an algorithm for selecting siRNA, said method comprising: (a) selecting a set of siRNA; (b) measuring gene silencing ability of each siRNA from said set; (c) determining relative functionality of each siRNA; (d) determining improved functionality by the presence or absence of at least one variable selected from the group consisting of the presence or absence of a particular nucleotide at a particular position, the total number of As and Us in positions 15-19, the number of times that the same nucleotide repeats within a given sequence, and the total number of Gs and Cs; and (e) developing an algorithm using the information of step (d).

According to a fifth embodiment, the present invention provides a kit, wherein said kit is comprised of at least two siRNAs, wherein said at least two siRNAs comprise a first optimized siRNA and a second optimized siRNA, wherein said first optimized siRNA and said second optimized siRNA are optimized according a formula comprising Formula X.

The present invention also provides a method for identifying a hyperfunctional siRNA, comprising applying selection criteria to a set of potential siRNA that comprise 18-30 base pairs, wherein said selection criteria are non-target specific criteria, and said set comprises at least two siRNAs and each of said at least two siRNAs contains a sequence that is at least substantially complementary to a target gene; determining the relative functionality of the at least two siRNAs and assigning each of the at least two siRNAs a functionality score; and selecting siRNAs from the at least two siRNAs that have a functionality score that reflects greater than 80 percent silencing at a concentration in the picomolar range, wherein said greater than 80 percent silencing endures for greater than 120 hours.

According to a sixth embodiment, the present invention provides a hyperfunctional siRNA that is capable of silencing Bcl2.

According to a seventh embodiment, the present invention provides a method for developing an siRNA algorithm for selecting functional and hyperfunctional siRNAs for a given sequence. The method comprises:

(a) selecting a set of siRNAs;

(b) measuring the gene silencing ability of each siRNA from said set;

(c) determining the relative functionality of each siRNA;

(d) determining the amount of improved functionality by the presence or absence of at least one variable selected from the group consisting of the total GC content, melting temperature of the siRNA, GC content at positions 15-19, the presence or absence of a particular nucleotide at a particular position, relative thermodynamic stability at particular positions in a duplex, and the number of times that the same nucleotide repeats within a given sequence; and

(e) developing an algorithm using the information of step (d).

According to this embodiment, preferably the set of siRNAs comprises at least 90 siRNAs from at least one gene, more preferably at least 180 siRNAs from at least two different genes, and most preferably at least 270 and 360 siRNAs from at least three and four different genes, respectively. Additionally, in step (d) the determination is made with preferably at least two, more preferably at least three, even more preferably at least four, and most preferably all of the variables. The resulting algorithm is not target sequence specific.

In another embodiment, the present invention provides rationally designed siRNAs identified using the formulas above.

In yet another embodiment, the present invention is directed to hyperfunctional siRNA.

The ability to use the above algorithms, which are not sequence or species specific, allows for the cost-effective selection of optimized siRNAs for specific target sequences. Accordingly, there will be both greater efficiency and reliability in the use of siRNA technologies.

In various embodiments, siRNAs that target nucleotide sequences for transient receptor (GCGR) are provided. In various embodiments, the siRNAs are rationally designed. In various embodiments, the siRNAs are functional or hyperfunctional.

In various embodiments, an siRNA that targets the nucleotide sequence for GCGR is provided, wherein the siRNA is selected from the group consisting of various siRNA sequences targeting the nucleotide sequences for GCGR that are disclosed herein. In various embodiments, the siRNA sequence is selected from the group consisting of SEQ ID NO. 438 to SEQ ID NO. 497.

In various embodiments, siRNA comprising a sense region and an antisense region are provided, said sense region and said antisense region together form a duplex region comprising 18-30 base pairs, and said sense region comprises a sequence that is at least 90% similar to a sequence selected from the group consisting of siRNA sequences targeting nucleotide sequences for GCGR that are disclosed herein. In various embodiments, the siRNA sequence is selected from the group consisting of SEQ ID NO. 438 to SEQ ID NO. 497.

In various embodiments, an siRNA comprising a sense region and an antisense region is provided, said sense region and said antisense region together form a duplex region comprising 18-30 base pairs, and said sense region comprises a sequence that is identical to a contiguous stretch of at least 18 bases of a sequence selected from the group consisting of SEQ ID NO. 438 to SEQ ID NO. 497. In various embodiments, the duplex region is 19-30 base pairs, and the sense region comprises a sequence that is identical to a sequence selected from the group consisting of SEQ ID NO. 438 to SEQ ID NO. 497.

In various embodiments, a pool of at least two siRNAs is provided, wherein said pool comprises a first siRNA and a second siRNA, said first siRNA comprising a duplex region of length 18-30 base pairs that has a first sense region that is at least 90% similar to 18 bases of a first sequence selected from the group consisting of SEQ ID NO. 438 to SEQ ID NO. 497, and said second siRNA comprises a duplex region of length 18-30 base pairs that has a second sense region that is at least 90% similar to 18 bases of a second sequence selected from the group consisting of SEQ ID NO. 438 to SEQ ID NO. 497, wherein said first sense region and said second sense region are not identical.

In various embodiments, the first sense region comprises a sequence that is identical to at least 18 bases of a sequence selected from the group consisting of SEQ ID NO. 438 to SEQ ID NO. 497, and said second sense region comprises a sequence that is identical to at least 18 bases of a sequence selected from the group consisting of SEQ ID NO. 438 to SEQ ID NO. 497. In various embodiments, the duplex of said first siRNA is 19-30 base pairs, and said first sense region comprises a sequence that is at least 90% similar to a sequence selected from the group consisting of SEQ ID NO. 438 to SEQ ID NO. 497, and said duplex of said second siRNA is 19-30 base pairs and comprises a sequence that is at least 90% similar to a sequence selected from the group consisting of SEQ ID NO. 438 to SEQ ID NO. 497.

In various embodiments, the duplex of said first siRNA is 19-30 base pairs and said first sense region comprises a sequence that is identical to at least 18 bases of a sequence selected from the group consisting of SEQ ID NO. 438 to SEQ ID NO. 497, and said duplex of said second siRNA is 19-30 base pairs and said second region comprises a sequence that is identical to a sequence selected from the group consisting of SEQ ID NO. 438 to SEQ ID NO. 497.

For a better understanding of the present invention together with other and further advantages and embodiments, reference is made to the following description taken in conjunction with the examples, the scope of which is set forth in the appended claims.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows a model for siRNA-RISC interactions. RISC has the ability to interact with either end of the siRNA or miRNA molecule. Following binding, the duplex is unwound, and the relevant target is identified, cleaved, and released.

FIG. 2 is a representation of the functionality of two hundred and seventy siRNA duplexes that were generated to target human cyclophilin, human diazepam-binding inhibitor (DB), and firefly luciferase.

FIG. 3 a is a representation of the silencing effect of 30 siRNAs in three different cells lines, HEK293, DU145, and Hela. FIG. 3 b shows the frequency of different functional groups (>95% silencing (black), >80% silencing (gray), >50% silencing (dark gray), and <50% silencing (white)) based on GC content. In cases where a given bar is absent from a particular GC percentage, no siRNA were identified for that particular group. FIG. 3 c shows the frequency of different functional groups based on melting temperature (Tm).

FIG. 4 is a representation of a statistical analysis that revealed correlations between silencing and five sequence-related properties of siRNA: (A) an A at position 19 of the sense strand, (B) an A at position 3 of the sense strand, (C) a U at position 10 of the sense strand, (D) a base other than G at position 13 of the sense strand, and (E) a base other than C at position 19 of the sense strand. All variables were correlated with siRNA silencing of firefly luciferase and human cyclophilin. siRNAs satisfying the criterion are grouped on the left (Selected) while those that do not, are grouped on the right (Eliminated). Y-axis is “% Silencing of Control.” Each position on the X-axis represents a unique siRNA.

FIGS. 5A and 5B are representations of firefly luciferase and cyclophilin siRNA panels sorted according to functionality and predicted values using Formula VIII. The siRNA found within the circle represent those that have Formula VIII values (SMARTSCORES™, or siRNA rank) above zero. siRNA outside the indicated area have calculated Formula VIII values that are below zero. Y-axis is “Expression (% Control).” Each position on the X-axis represents a unique siRNA.

FIG. 6A is a representation of the average internal stability profile (AISP) derived from 270 siRNAs taken from three separate genes (cyclophilin B, DBI and firefly luciferase). Graphs represent AISP values of highly functional, functional, and non-functional siRNA. FIG. 6B is a comparison between the AISP of naturally derived GFP siRNA (filled squares) and the AISP of siRNA from cyclophilin B, DBI, and luciferase having >90% silencing properties (no fill) for the antisense strand. “DG” is the symbol for ΔG, free energy.

FIG. 7 is a histogram showing the differences in duplex functionality upon introduction of base pair mismatches. The X-axis shows the mismatch introduced in the siRNA and the position it is introduced (e.g., 8C>A reveals that position 8 (which normally has a C) has been changed to an A). The Y-axis is “% Silencing (Normalized to Control).” The samples on the X-axis represent siRNAs at 100 nM and are, reading from left to right: 1A to C, 1A to G, 1A to U; 2A to C, 2A to G, 2A to U; 3A to C, 3A to G, 3A to U; 4G to A, 4G to C; 4G to U; 5U to A, 5U to C, 5U to G; 6U to A, 6U to C, 6U to G; 7G to A, 7G to C, 7G to U; 8C to A, 8C to G, 8C to U; 9G to A, 9G to C, 9G to U; 10C to A, 10C to G, 10C to U; 11G to A, 11G to C, 11G to U; 12G to A, 12G to C, 12G to U; 13A to C, 13A to G, 13A to U; 14G to A, 14G to C, 14G to U; 15G to A, 15G to C, 15G to U; 16A to C, 16A to G, 16A to U; 17G to A, 17G to C, 17G to U; 18U to A, 18U to C, 18U to G; 19U to A, 19U to C, 19U to G; 20 wt; Control.

FIG. 8 is histogram that shows the effects of 5′ sense and antisense strand modification with 2′-O-methylation on functionality.

FIG. 9 shows a graph of SMARTSCORES™, or siRNA rank, versus RNAi silencing values for more than 360 siRNA directed against 30 different genes. SiRNA to the right of the vertical bar represent those siRNA that have desirable SMARTSCORES™, or siRNA rank.

FIGS. 10A-E compare the RNAi of five different genes (SEAP, DBI, PLK, Firefly Luciferase, and Renilla Luciferase) by varying numbers of randomly selected siRNA and four rationally designed (SMART-selected) siRNA chosen using the algorithm described in Formula VIII. In addition, RNAi induced by a pool of the four SMART-selected siRNA is reported at two different concentrations (100 and 400 nM). 10F is a comparison between a pool of randomly selected EGFR siRNA (Pool 1) and a pool of SMART-selected EGFR siRNA (Pool 2). Pool 1, S1-S4 and Pool 2 S1-S4 represent the individual members that made up each respective pool. Note that numbers for random siRNAs represent the position of the 5′ end of the sense strand of the duplex. The Y-axis represents the % expression of the control(s). The X-axis is the percent expression of the control.

FIG. 11 shows the Western blot results from cells treated with siRNA directed against twelve different genes involved in the clathrin-dependent endocytosis pathway (CHC, Dynil, CALM, CLCa, CLCb, Eps15, Eps15R, Rab5a, Rab5b, Rab5c, β2 subunit of AP-2 and EEA.1). siRNA were selected using Formula VIII. “Pool” represents a mixture of duplexes 1-4. Total concentration of each siRNA in the pool is 25 nM. Total concentration=4×25=100 nM.

FIG. 12 is a representation of the gene silencing capabilities of rationally-selected siRNA directed against ten different genes (human and mouse cyclophilin, C-myc, human lamin A/C, QB (ubiquinol-cytochrome c reductase core protein 1), MEK1 and MEK2, ATE1 (arginyl-tRNA protein transferase), GAPDH, and Eg5). The Y-axis is the percent expression of the control. Numbers 1, 2, 3 and 4 represent individual rationally selected siRNA. “Pool” represents a mixture of the four individual siRNA.

FIG. 13 is the sequence of the top ten Bcl2 siRNAs as determined by Formula VIII. Sequences are listed 5′ to 3′.

FIG. 14 is the knockdown by the top ten Bcl2 siRNAs at 100 nM concentrations. The Y-axis represents the amount of expression relative to the non-specific (ns) and transfection mixture control.

FIG. 15 represents a functional walk where siRNA beginning on every other base pair of a region of the luciferase gene are tested for the ability to silence the luciferase gene. The Y-axis represents the percent expression relative to a control. The X-axis represents the position of each individual siRNA. Reading from left to right across the X-axis, the position designations are 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, and Plasmid.

FIGS. 16A and 16B are histograms demonstrating the inhibition of target gene expression by pools of 2 (16A) and 3 (16B) siRNA duplexes taken from the walk described in FIG. 15. The Y-axis in each represents the percent expression relative to control. The X-axis in each represents the position of the first siRNA in paired pools, or trios of siRNAs. For instance, the first paired pool contains siRNAs 1 and 3. The second paired pool contains siRNAs 3 and 5. Pool 3 (of paired pools) contains siRNAs 5 and 7, and so on. For each of 16A and 16B, the X-axis from left to right reads 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, and Plasmid.

FIGS. 17A and 17B are histograms demonstrating the inhibition of target gene expression by pools of 4 (17A) and 5 (17B) siRNA duplexes. The Y-axis in each represents the percent expression relative to control. The X-axis in each represents the position of the first siRNA in each pool. For each of 17A and 17B, the X-axis from left to right reads 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, and Plasmid.

FIGS. 18A and 18B are histograms demonstrating the inhibition of target gene expression by siRNAs that are ten (18A) and twenty (18B) base pairs base pairs apart. The Y-axis represents the percent expression relative to a control. The X-axis represents the position of the first siRNA in each pool. For each of 18A and 18B, the X-axis from left to right reads 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, and Plasmid.

FIG. 19 shows that pools of siRNAs (dark gray bar) work as well (or better) than the best siRNA in the pool (light gray bar). The Y-axis represents the percent expression relative to a control. The X-axis represents the position of the first siRNA in each pool. The X-axis from left to right reads 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, and Plasmid.

FIG. 20 shows that the combination of several semifunctional siRNAs (dark gray) result in a significant improvement of gene expression inhibition over individual (semi-functional; light gray) siRNA. The Y-axis represents the percent expression relative to a control.

FIGS. 21A, 21B and 21C show both pools (Library, Lib) and individual siRNAs in inhibition of gene expression of Beta-Galactosidase, Renilla Luciferase and SEAP (alkaline phosphatase). Numbers on the X-axis indicate the position of the 5′-most nucleotide of the sense strand of the duplex. The Y-axis represents the percent expression of each gene relative to a control. Libraries contain 19 nucleotide long siRNAs (not including overhangs) that begin at the following nucleotides: SEAP: Lib 1: 206, 766, 812, 923, Lib 2: 1117, 1280, 1300, 1487, Lib 3: 206, 766, 812, 923, 1117, 1280, 1300, 1487, Lib 4: 206, 812, 1117, 1300, Lib 5: 766, 923, 1280, 1487, Lib 6: 206, 1487; Bgal: Lib 1: 979, 1339, 2029, 2590, Lib 2: 1087, 1783, 2399, 3257, Lib 3: 979, 1783, 2590, 3257, Lib 4: 979, 1087, 1339, 1783, 2029, 2399, 2590, 3257, Lib 5: 979, 1087, 1339, 1783, Lib 6: 2029, 2399, 2590, 3257; Renilla: Lib 1: 174, 300, 432, 568, Lib 2: 592, 633, 729, 867, Lib 3: 174, 300, 432, 568, 592, 633, 729, 867, Lib 4: 174, 432, 592, 729, Lib 5: 300, 568, 633, 867, Lib 6: 592, 568.

FIG. 22 shows the results of an EGFR and TfnR internalization assay when single gene knockdowns are performed. The Y-axis represents percent internalization relative to control.

FIG. 23 shows the results of an EGFR and TfnR internalization assay when multiple genes are knocked down (e.g., Rab5a, b, c). The Y-axis represents the percent internalization relative to control.

FIG. 24 shows the simultaneous knockdown of four different genes. siRNAs directed against G6PD, GAPDH, PLK, and UQC were simultaneously introduced into cells. Twenty-four hours later, cultures were harvested and assayed for mRNA target levels for each of the four genes. A comparison is made between cells transfected with individual siRNAs vs. a pool of siRNAs directed against all four genes.

FIG. 25 shows the functionality of ten siRNAs at 0.3 nM concentrations.

DETAILED DESCRIPTION Definitions

Unless stated otherwise, the following terms and phrases have the meanings provided below:

Complementary

The term “complementary” refers to the ability of polynucleotides to form base pairs with one another. Base pairs are typically formed by hydrogen bonds between nucleotide units in antiparallel polynucleotide strands. Complementary polynucleotide strands can base pair in the Watson-Crick manner (e.g., A to T, A to U, C to G), or in any other manner that allows for the formation of duplexes. As persons skilled in the art are aware, when using RNA as opposed to DNA, uracil rather than thymine is the base that is considered to be complementary to adenosine. However, when a U is denoted in the context of the present invention, the ability to substitute a T is implied, unless otherwise stated.

Perfect complementarity or 100% complementarity refers to the situation in which each nucleotide unit of one polynucleotide strand can hydrogen bond with a nucleotide unit of a second polynucleotide strand. Less than perfect complementarity refers to the situation in which some, but not all, nucleotide units of two strands can hydrogen bond with each other. For example, for two 20-mers, if only two base pairs on each strand can hydrogen bond with each other, the polynucleotide strands exhibit 10% complementarity. In the same example, if 18 base pairs on each strand can hydrogen bond with each other, the polynucleotide strands exhibit 90% complementarity.

Deoxynucleotide

The term “deoxynucleotide” refers to a nucleotide or polynucleotide lacking a hydroxyl group (OH group) at the 2′ and/or 3′ position of a sugar moiety. Instead, it has a hydrogen bonded to the 2′ and/or 3′ carbon. Within an RNA molecule that comprises one or more deoxynucleotides, “deoxynucleotide” refers to the lack of an OH group at the 2′ position of the sugar moiety, having instead a hydrogen bonded directly to the 2′ carbon.

Deoxyribonucleotide

The terms “deoxyribonucleotide” and “DNA” refer to a nucleotide or polynucleotide comprising at least one sugar moiety that has an H, rather than an OH, at its 2′ and/or 3′position.

Duplex Region

The phrase “duplex region” refers to the region in two complementary or substantially complementary polynucleotides that form base pairs with one another, either by Watson-Crick base pairing or any other manner that allows for a stabilized duplex between polynucleotide strands that are complementary or substantially complementary. For example, a polynucleotide strand having 21 nucleotide units can base pair with another polynucleotide of 21 nucleotide units, yet only 19 bases on each strand are complementary or substantially complementary, such that the “duplex region” has 19 base pairs. The remaining bases may, for example, exist as 5′ and 3′ overhangs. Further, within the duplex region, 100% complementarity is not required; substantial complementarity is allowable within a duplex region. Substantial complementarity refers to 79% or greater complementarity. For example, a mismatch in a duplex region consisting of 19 base pairs results in 94.7% complementarity, rendering the duplex region substantially complementary.

Filters

The term “filter” refers to one or more procedures that are performed on sequences that are identified by the algorithm. In some instances, filtering includes in silico procedures where sequences identified by the algorithm can be screened to identify duplexes carrying desirable or undesirable motifs. Sequences carrying such motifs can be selected for, or selected against, to obtain a final set with the preferred properties. In other instances, filtering includes wet lab experiments. For instance, sequences identified by one or more versions of the algorithm can be screened using any one of a number of procedures to identify duplexes that have hyperfunctional traits (e.g., they exhibit a high degree of silencing at subnanomolar concentrations and/or exhibit high degrees of silencing longevity).

Gene Silencing

The phrase “gene silencing” refers to a process by which the expression of a specific gene product is lessened or attenuated. Gene silencing can take place by a variety of pathways. Unless specified otherwise, as used herein, gene silencing refers to decreases in gene product expression that results from RNA interference (RNAi), a defined, though partially characterized pathway whereby small inhibitory RNA (siRNA) act in concert with host proteins (e.g., the RNA induced silencing complex, RISC) to degrade messenger RNA (mRNA) in a sequence-dependent fashion. The level of gene silencing can be measured by a variety of means, including, but not limited to, measurement of transcript levels by Northern Blot Analysis, B-DNA techniques, transcription-sensitive reporter constructs, expression profiling (e.g., DNA chips), and related technologies. Alternatively, the level of silencing can be measured by assessing the level of the protein encoded by a specific gene. This can be accomplished by performing a number of studies including Western Analysis, measuring the levels of expression of a reporter protein that has e.g., fluorescent properties (e.g., GFP) or enzymatic activity (e.g., alkaline phosphatases), or several other procedures.

miRNA

The term “miRNA” refers to microRNA.

Nucleotide

The term “nucleotide” refers to a ribonucleotide or a deoxyribonucleotide or modified form thereof, as well as an analog thereof. Nucleotides include species that comprise purines, e.g., adenine, hypoxanthine, guanine, and their derivatives and analogs, as well as pyrimidines, e.g., cytosine, uracil, thymine, and their derivatives and analogs.

Nucleotide analogs include nucleotides having modifications in the chemical structure of the base, sugar and/or phosphate, including, but not limited to, 5-position pyrimidine modifications, 8-position purine modifications, modifications at cytosine exocyclic amines, and substitution of 5-bromo-uracil; and 2′-position sugar modifications, including but not limited to, sugar-modified ribonucleotides in which the 2′-OH is replaced by a group such as an H, OR, R, halo, SH, SR, NH₂, NHR, NR₂, or CN, wherein R is an alkyl moiety. Nucleotide analogs are also meant to include nucleotides with bases such as inosine, queuosine, xanthine, sugars such as 2′-methyl ribose, non-natural phosphodiester linkages such as methylphosphonates, phosphorothioates and peptides.

Modified bases refer to nucleotide bases such as, for example, adenine, guanine, cytosine, thymine, uracil, xanthine, inosine, and queuosine that have been modified by the replacement or addition of one or more atoms or groups. Some examples of types of modifications that can comprise nucleotides that are modified with respect to the base moieties include but are not limited to, alkylated, halogenated, thiolated, aminated, amidated, or acetylated bases, individually or in combination. More specific examples include, for example, 5-propynyluridine, 5-propynylcytidine, 6-methyladenine, 6-methylguanine, N,N,-dimethyladenine, 2-propyladenine, 2-propylguanine, 2-aminoadenine, 1-methylinosine, 3-methyluridine, 5-methylcytidine, 5-methyluridine and other nucleotides having a modification at the 5 position, 5-(2-amino)propyl uridine, 5-halocytidine, 5-halouridine, 4-acetylcytidine, 1-methyladenosine, 2-methyladenosine, 3-methylcytidine, 6-methyluridine, 2-methylguanosine, 7-methylguanosine, 2,2-dimethylguanosine, 5-methylaminoethyluridine, 5-methyloxyuridine, deazanucleotides such as 7-deaza-adenosine, 6-azouridine, 6-azocytidine, 6-azothymidine, 5-methyl-2-thiouridine, other thio bases such as 2-thiouridine and 4-thiouridine and 2-thiocytidine, dihydrouridine, pseudouridine, queuosine, archaeosine, naphthyl and substituted naphthyl groups, any O- and N-alkylated purines and pyrimidines such as N6-methyladenosine, 5-methylcarbonylmethyluridine, uridine 5-oxyacetic acid, pyridine-4-one, pyridine-2-one, phenyl and modified phenyl groups such as aminophenol or 2,4,6-trimethoxy benzene, modified cytosines that act as G-clamp nucleotides, 8-substituted adenines and guanines, 5-substituted uracils and thymines, azapyrimidines, carboxyhydroxyalkyl nucleotides, carboxyalkylaminoalkyl nucleotides, and alkylcarbonylalkylated nucleotides. Modified nucleotides also include those nucleotides that are modified with respect to the sugar moiety, as well as nucleotides having sugars or analogs thereof that are not ribosyl. For example, the sugar moieties may be, or be based on, mannoses, arabinoses, glucopyranoses, galactopyranoses, 4′-thioribose, and other sugars, heterocycles, or carbocycles.

The term nucleotide is also meant to include what are known in the art as universal bases. By way of example, universal bases include but are not limited to 3-nitropyrrole, 5-nitroindole, or nebularine. The term “nucleotide” is also meant to include the N3′ to P5′ phosphoramidate, resulting from the substitution of a ribosyl 3′ oxygen with an amine group.

Further, the term nucleotide also includes those species that have a detectable label, such as for example a radioactive or fluorescent moiety, or mass label attached to the nucleotide.

Off-Target Silencing and Off-Target Interference

The phrases “off-target silencing” and “off-target interference” are defined as degradation of mRNA other than the intended target mRNA due to overlapping and/or partial homology with secondary mRNA messages.

Polynucleotide

The term “polynucleotide” refers to polymers of nucleotides, and includes but is not limited to DNA, RNA, DNA/RNA hybrids including polynucleotide chains of regularly and/or irregularly alternating deoxyribosyl moieties and ribosyl moieties (i.e., wherein alternate nucleotide units have an —OH, then and —H, then an —OH, then an —H, and so on at the 2′ position of a sugar moiety), and modifications of these kinds of polynucleotides, wherein the attachment of various entities or moieties to the nucleotide units at any position are included.

Polyribonucleotide

The term “polyribonucleotide” refers to a polynucleotide comprising two or more modified or unmodified ribonucleotides and/or their analogs. The term “polyribonucleotide” is used interchangeably with the term “oligoribonucleotide.”

Ribonucleotide and Ribonucleic Acid

The term “ribonucleotide” and the phrase “ribonucleic acid” (RNA), refer to a modified or unmodified nucleotide or polynucleotide comprising at least one ribonucleotide unit. A ribonucleotide unit comprises an hydroxyl group attached to the 2′ position of a ribosyl moiety that has a nitrogenous base attached in N-glycosidic linkage at the 1′ position of a ribosyl moiety, and a moiety that either allows for linkage to another nucleotide or precludes linkage.

siRNA

The term “siRNA” refers to small inhibitory RNA duplexes that induce the RNA interference (RNAi) pathway. These molecules can vary in length (generally 18-30 base pairs) and contain varying degrees of complementarity to their target mRNA in the antisense strand. Some, but not all, siRNA have unpaired overhanging bases on the 5′ or 3′ end of the sense strand and/or the antisense strand. The term “siRNA” includes duplexes of two separate strands, as well as single strands that can form hairpin structures comprising a duplex region.

siRNA may be divided into five (5) groups (non-functional, semi-functional, functional, highly functional, and hyper-functional) based on the level or degree of silencing that they induce in cultured cell lines. As used herein, these definitions are based on a set of conditions where the siRNA is transfected into said cell line at a concentration of 100 nM and the level of silencing is tested at a time of roughly 24 hours after transfection, and not exceeding 72 hours after transfection. In this context, “non-functional siRNA” are defined as those siRNA that induce less than 50% (<50%) target silencing. “Semi-functional siRNA” induce 50-79% target silencing. “Functional siRNA” are molecules that induce 80-95% gene silencing. “Highly-functional siRNA” are molecules that induce greater than 95% gene silencing. “Hyperfunctional siRNA” are a special class of molecules. For purposes of this document, hyperfunctional siRNA are defined as those molecules that: (1) induce greater than 95% silencing of a specific target when they are transfected at subnanomolar concentrations (i.e., less than one nanomolar); and/or (2) induce functional (or better) levels of silencing for greater than 96 hours. These relative functionalities (though not intended to be absolutes) may be used to compare siRNAs to a particular target for applications such as functional genomics, target identification and therapeutics.

SMARTSCORE™, or siRNA Rank

The term “SMARTSCORE™”, or “siRNA rank” refers to a number determined by applying any of the formulas to a given siRNA sequence. The term “SMART-selected” or “rationally selected” or “rational selection” refers to siRNA that have been selected on the basis of their SMARTSCORES™, or siRNA ranking.

Substantially Similar

The phrase “substantially similar” refers to a similarity of at least 90% with respect to the identity of the bases of the sequence.

Target

The term “target” is used in a variety of different forms throughout this document and is defined by the context in which it is used. “Target mRNA” refers to a messenger RNA to which a given siRNA can be directed against. “Target sequence” and “target site” refer to a sequence within the mRNA to which the sense strand of an siRNA shows varying degrees of homology and the antisense strand exhibits varying degrees of complementarity. The phrase “siRNA target” can refer to the gene, mRNA, or protein against which an siRNA is directed. Similarly, “target silencing” can refer to the state of a gene, or the corresponding mRNA or protein.

Transfection

The term “transfection” refers to a process by which agents are introduced into a cell. The list of agents that can be transfected is large and includes, but is not limited to, siRNA, sense and/or anti-sense sequences, DNA encoding one or more genes and organized into an expression plasmid, proteins, protein fragments, and more. There are multiple methods for transfecting agents into a cell including, but not limited to, electroporation, calcium phosphate-based transfections, DEAE-dextran-based transfections, lipid-based transfections, molecular conjugate-based transfections (e.g., polylysine-DNA conjugates), microinjection and others.

The present invention is directed to improving the efficiency of gene silencing by siRNA. Through the inclusion of multiple siRNA sequences that are targeted to a particular gene and/or selecting an siRNA sequence based on certain defined criteria, improved efficiency may be achieved.

The present invention will now be described in connection with preferred embodiments. These embodiments are presented in order to aid in an understanding of the present invention and are not intended, and should not be construed, to limit the invention in any way. All alternatives, modifications and equivalents that may become apparent to those of ordinary skill upon reading this disclosure are included within the spirit and scope of the present invention.

Furthermore, this disclosure is not a primer on RNA interference. Basic concepts known to persons skilled in the art have not been set forth in detail.

The present invention is directed to increasing the efficiency of RNAi, particularly in mammalian systems. Accordingly, the present invention provides kits, siRNAs and methods for increasing siRNA efficacy.

According to a first embodiment, the present invention provides a kit for gene silencing, wherein said kit is comprised of a pool of at least two siRNA duplexes, each of which is comprised of a sequence that is complementary to a portion of the sequence of one or more target messenger RNA, and each of which is selected using non-target specific criteria. Each of the at least two siRNA duplexes of the kit complementary to a portion of the sequence of one or more target mRNAs is preferably selected using Formula X.

According to a second embodiment, the present invention provides a method for selecting an siRNA, said method comprising applying selection criteria to a set of potential siRNA that comprise 18-30 base pairs, wherein said selection criteria are non-target specific criteria, and said set comprises at least two siRNAs and each of said at least two siRNAs contains a sequence that is at least substantially complementary to a target gene; and determining the relative functionality of the at least two siRNAs.

In one embodiment, the present invention also provides a method wherein said selection criteria are embodied in a formula comprising: (−14)*G₁₃−13*A₁−12*U₇−11*U₂−10*A₁₁−10*U₄−10*C₃−10*C₅−10*C₆−9*A₁₀− 9*U₉−9*C₁₈−8*G₁₀−7*U₁−7*U₁₆−7*C₁₇−7*C₁₉+7*U₁₇+8*A₂+8*A₄+8*A₅+8*C₄ +9*G₈+10*A₇+10*U₁₈+11*A₁₉+11*C₉+15*G₁+18*A₃+19*U₁₀−Tm−3*(GC_(total)) −6*(GC₁₅₋₁₉)−30*X; or  Formula VIII (−8)*A1+(−1)*A2+(12)*A3+(7)*A4+(18)*A5+(12)*A6+ (19)*A7+(6)*A8+(−4)*A9+(−5)*A10+(−2)*A11+(−5)*A12+(17)*A13+(− 3)*A14+(4)*A15+(2)*A16+(8)*A17+(11)*A18+(30)*A19+(−13)*U1+(− 10)*U2+(2)*U3+(−2)*U4+(−5)*U5+(5)*U6+(−2)*U7+(−10)*U8+(− 5)*U9+(15)*U10+(−1)*U11+(0)*U12+(10)*U13+(−9)*U14+(−13)*U15+(− 10)*U16+(3)*U17+(9)*U18+(9)*U19+(7)*C1+(3)*C2+(−21)*C3+(5)*C4+(− 9)*C5+(−20)*C6+(−18)*C7+(−5)*C8+(5)*C9+(1)*C10+(2)*C11+(− 5)*C12+(−3)*C13+(−6)*C14+(−2)*C15+(−5)*C16+(−3)*C17+(−12)*C18+(− 18)*C19+(14)*G1+(8)*G2+(7)*G3+(−10)*G4+(− 4)*G5+(2)*G6+(1)*G7+(9)*G8+(5)*G9+(−11)*G10+(1)*G11+(9)*G12+(− 24)*G13+(18)*G14+(11)*G15+(13)*G16+(−7)*G17+(−9)*G18+(−22)*G19+ 6*(number of A+U in position 15-19)−3*(number of G+C in whole siRNA),  Formula X

wherein position numbering begins at the 5′-most position of a sense strand, and

A₁=1 if A is the base at position 1 of the sense strand, otherwise its value is 0;

A₂=1 if A is the base at position 2 of the sense strand, otherwise its value is 0;

A₃=1 if A is the base at position 3 of the sense strand, otherwise its value is 0;

A₄=1 if A is the base at position 4 of the sense strand, otherwise its value is 0;

A₅=1 if A is the base at position 5 of the sense strand, otherwise its value is 0;

A₆=1 if A is the base at position 6 of the sense strand, otherwise its value is 0;

A₇=1 if A is the base at position 7 of the sense strand, otherwise its value is 0;

A₁₀=1 if A is the base at position 10 of the sense strand, otherwise its value is 0;

A₁₁=1 if A is the base at position 11 of the sense strand, otherwise its value is 0;

A₁₃=1 if A is the base at position 13 of the sense strand, otherwise its value is 0;

A₁₉=1 if A is the base at position 19 of the sense strand, otherwise if another base is present or the sense strand is only 18 base pairs in length, its value is 0;

C₃=1 if C is the base at position 3 of the sense strand, otherwise its value is 0;

C₄=1 if C is the base at position 4 of the sense strand, otherwise its value is 0;

C₅=1 if C is the base at position 5 of the sense strand, otherwise its value is 0;

C₆=1 if C is the base at position 6 of the sense strand, otherwise its value is 0;

C₇=1 if C is the base at position 7 of the sense strand, otherwise its value is 0;

C₉=1 if C is the base at position 9 of the sense strand, otherwise its value is 0;

C₁₇=1 if C is the base at position 17 of the sense strand, otherwise its value is 0;

C₁₈=1 if C is the base at position 18 of the sense strand, otherwise its value is 0;

C₁₉=1 if C is the base at position 19 of the sense strand, otherwise if another base is present or the sense strand is only 18 base pairs in length, its value is 0;

G₁=1 if G is the base at position 1 on the sense strand, otherwise its value is 0;

G₂=1 if G is the base at position 2 of the sense strand, otherwise its value is 0;

G₈=1 if G is the base at position 8 on the sense strand, otherwise its value is 0;

G₁₀=1 if G is the base at position 10 on the sense strand, otherwise its value is 0;

G₁₃=1 if G is the base at position 13 on the sense strand, otherwise its value is 0;

G₁₉=1 if G is the base at position 19 of the sense strand, otherwise if another base is present or the sense strand is only 18 base pairs in length, its value is 0;

U₁=1 if U is the base at position 1 on the sense strand, otherwise its value is 0;

U₂=1 if U is the base at position 2 on the sense strand, otherwise its value is 0;

U₃=1 if U is the base at position 3 on the sense strand, otherwise its value is 0;

U₄=1 if U is the base at position 4 on the sense strand, otherwise its value is 0;

U₇=1 if U is the base at position 7 on the sense strand, otherwise its value is 0;

U₉=1 if U is the base at position 9 on the sense strand, otherwise its value is 0;

U₁₀=1 if U is the base at position 10 on the sense strand, otherwise its value is 0;

U₁₅=1 if U is the base at position 15 on the sense strand, otherwise its value is 0;

U₁₆=1 if U is the base at position 16 on the sense strand, otherwise its value is 0;

U₁₇=1 if U is the base at position 17 on the sense strand, otherwise its value is 0;

U₁₈=1 if U is the base at position 18 on the sense strand, otherwise its value is 0.

GC₁₅₋₁₉=the number of G and C bases within positions 15-19 of the sense strand, or within positions 15-18 if the sense strand is only 18 base pairs in length;

GC_(total)=the number of G and C bases in the sense strand;

Tm=100 if the siRNA oligo has the internal repeat longer then 4 base pairs, otherwise its value is 0; and

X=the number of times that the same nucleotide repeats four or more times in a row.

Any of the methods of selecting siRNA in accordance with the invention can further comprise comparing the internal stability profiles of the siRNAs to be selected, and selecting those siRNAs with the most favorable internal stability profiles. Any of the methods of selecting siRNA can further comprise selecting either for or against sequences that contain motifs that induce cellular stress. Such motifs include, for example, toxicity motifs. Any of the methods of selecting siRNA can further comprise either selecting for or selecting against sequences that comprise stability motifs.

In another embodiment, the present invention provides a method of gene silencing, comprising introducing into a cell at least one siRNA selected according to any of the methods of the present invention. The siRNA can be introduced by allowing passive uptake of siRNA, or through the use of a vector.

According to a third embodiment, the invention provides a method for developing an algorithm for selecting siRNA, said method comprising: (a) selecting a set of siRNA; (b) measuring gene silencing ability of each siRNA from said set; (c) determining relative functionality of each siRNA; (d) determining improved functionality by the presence or absence of at least one variable selected from the group consisting of the presence or absence of a particular nucleotide at a particular position, the total number of As and Us in positions 15-19, the number of times that the same nucleotide repeats within a given sequence, and the total number of Gs and Cs; and (e) developing an algorithm using the information of step (d).

In another embodiment, the invention provides a method for selecting an siRNA with improved functionality, comprising using the above-mentioned algorithm to identify an siRNA of improved functionality.

According to a fourth embodiment, the present invention provides a kit, wherein said kit is comprised of at least two siRNAs, wherein said at least two siRNAs comprise a first optimized siRNA and a second optimized siRNA, wherein said first optimized siRNA and said second optimized siRNA are optimized according a formula comprising Formula X.

According to a fifth embodiment, the present invention provides a method for identifying a hyperfunctional siRNA, comprising applying selection criteria to a set of potential siRNA that comprise 18-30 base pairs, wherein said selection criteria are non-target specific criteria, and said set comprises at least two siRNAs and each of said at least two siRNAs contains a sequence that is at least substantially complementary to a target gene; determining the relative functionality of the at least two siRNAs and assigning each of the at least two siRNAs a functionality score; and selecting siRNAs from the at least two siRNAs that have a functionality score that reflects greater than 80 percent silencing at a concentration in the picomolar range, wherein said greater than 80 percent silencing endures for greater than 120 hours.

In other embodiments, the invention provides kits and/or methods wherein the siRNA are comprised of two separate polynucleotide strands; wherein the siRNA are comprised of a single contiguous molecule such as, for example, a unimolecular siRNA (comprising, for example, either a nucleotide or non-nucleotide loop); wherein the siRNA are expressed from one or more vectors; and wherein two or more genes are silenced by a single administration of siRNA.

According to a sixth embodiment, the present invention provides a hyperfunctional siRNA that is capable of silencing Bcl2.

According to a seventh embodiment, the present invention provides a method for developing an siRNA algorithm for selecting functional and hyperfunctional siRNAs for a given sequence. The method comprises:

(a) selecting a set of siRNAs;

(b) measuring the gene silencing ability of each siRNA from said set;

(c) determining the relative functionality of each siRNA;

(d) determining the amount of improved functionality by the presence or absence of at least one variable selected from the group consisting of the total GC content, melting temperature of the siRNA, GC content at positions 15-19, the presence or absence of a particular nucleotide at a particular position, relative thermodynamic stability at particular positions in a duplex, and the number of times that the same nucleotide repeats within a given sequence; and

(e) developing an algorithm using the information of step (d).

According to this embodiment, preferably the set of siRNAs comprises at least 90 siRNAs from at least one gene, more preferably at least 180 siRNAs from at least two different genes, and most preferably at least 270 and 360 siRNAs from at least three and four different genes, respectively. Additionally, in step (d) the determination is made with preferably at least two, more preferably at least three, even more preferably at least four, and most preferably all of the variables. The resulting algorithm is not target sequence specific.

In another embodiment, the present invention provides rationally designed siRNAs identified using the formulas above.

In yet another embodiment, the present invention is directed to hyperfunctional siRNA.

The ability to use the above algorithms, which are not sequence or species specific, allows for the cost-effective selection of optimized siRNAs for specific target sequences. Accordingly, there will be both greater efficiency and reliability in the use of siRNA technologies.

The methods disclosed herein can be used in conjunction with comparing internal stability profiles of selected siRNAs, and designing an siRNA with a desirable internal stability profile; and/or in conjunction with a selection either for or against sequences that contain motifs that induce cellular stress, for example, cellular toxicity.

Any of the methods disclosed herein can be used to silence one or more genes by introducing an siRNA selected, or designed, in accordance with any of the methods disclosed herein. The siRNA(s) can be introduced into the cell by any method known in the art, including passive uptake or through the use of one or more vectors.

Any of the methods and kits disclosed herein can employ either unimolecular siRNAs, siRNAs comprised of two separate polynucleotide strands, or combinations thereof. Any of the methods disclosed herein can be used in gene silencing, where two or more genes are silenced by a single administration of siRNA(s). The siRNA(s) can be directed against two or more target genes, and administered in a single dose or single transfection, as the case may be.

Optimizing siRNA

According to one embodiment, the present invention provides a method for improving the effectiveness of gene silencing for use to silence a particular gene through the selection of an optimal siRNA. An siRNA selected according to this method may be used individually, or in conjunction with the first embodiment, i.e., with one or more other siRNAs, each of which may or may not be selected by this criteria in order to maximize their efficiency.

The degree to which it is possible to select an siRNA for a given mRNA that maximizes these criteria will depend on the sequence of the mRNA itself. However, the selection criteria will be independent of the target sequence. According to this method, an siRNA is selected for a given gene by using a rational design. That said, rational design can be described in a variety of ways. Rational design is, in simplest terms, the application of a proven set of criteria that enhance the probability of identifying a functional or hyperfunctional siRNA. In one method, rationally designed siRNA can be identified by maximizing one or more of the following criteria:

(1) A low GC content, preferably between about 30-52%.

(2) At least 2, preferably at least 3 A or U bases at positions 15-19 of the siRNA on the sense strand.

(3) An A base at position 19 of the sense strand.

(4) An A base at position 3 of the sense strand.

(5) A U base at position 10 of the sense strand.

(6) An A base at position 14 of the sense strand.

(7) A base other than C at position 19 of the sense strand.

(8) A base other than G at position 13 of the sense strand.

(9) A Tm, which refers to the character of the internal repeat that results in inter- or intramolecular structures for one strand of the duplex, that is preferably not stable at greater than 50° C., more preferably not stable at greater than 37° C., even more preferably not stable at greater than 30° C. and most preferably not stable at greater than 20° C.

(10) A base other than U at position 5 of the sense strand.

(11) A base other than A at position 11 of the sense strand.

(12) A base other than an A at position 1 of the sense strand.

(13) A base other than an A at position 2 of the sense strand.

(14) An A base at position 4 of the sense strand.

(15) An A base at position 5 of the sense strand.

(16) An A base at position 6 of the sense strand.

(17) An A base at position 7 of the sense strand.

(18) An A base at position 8 of the sense strand.

(19) A base other than an A at position 9 of the sense strand.

(20) A base other than an A at position 10 of the sense strand.

(21) A base other than an A at position 11 of the sense strand.

(22) A base other than an A at position 12 of the sense strand.

(23) An A base at position 13 of the sense strand.

(24) A base other than an A at position 14 of the sense strand.

(25) An A base at position 15 of the sense strand

(26) An A base at position 16 of the sense strand.

(27) An A base at position 17 of the sense strand.

(28) An A base at position 18 of the sense strand.

(29) A base other than a U at position 1 of the sense strand.

(30) A base other than a U at position 2 of the sense strand.

(31) A U base at position 3 of the sense strand.

(32) A base other than a U at position 4 of the sense strand.

(33) A base other than a U at position 5 of the sense strand.

(34) A U base at position 6 of the sense strand.

(35) A base other than a U at position 7 of the sense strand.

(36) A base other than a U at position 8 of the sense strand.

(37) A base other than a U at position 9 of the sense strand.

(38) A base other than a U at position 1 of the sense strand.

(39) A U base at position 13 of the sense strand.

(40) A base other than a U at position 14 of the sense strand.

(41) A base other than a U at position 15 of the sense strand.

(42) A base other than a U at position 16 of the sense strand.

(43) A U base at position 17 of the sense strand.

(44) A U base at position 18 of the sense strand.

(45) A U base at position 19 of the sense strand.

(46) A C base at position 1 of the sense strand.

(47) A C base at position 2 of the sense strand.

(48) A base other than a C at position 3 of the sense strand.

(49) A C base at position 4 of the sense strand.

(50) A base other than a C at position 5 of the sense strand.

(51) A base other than a C at position 6 of the sense strand.

(52) A base other than a C at position 7 of the sense strand.

(53) A base other than a C at position 8 of the sense strand.

(54) A C base at position 9 of the sense strand.

(55) A C base at position 10 of the sense strand.

(56) A C base at position 11 of the sense strand.

(57) A base other than a C at position 12 of the sense strand.

(58) A base other than a C at position 13 of the sense strand.

(59) A base other than a C at position 14 of the sense strand.

(60) A base other than a C at position 15 of the sense strand.

(61) A base other than a C at position 16 of the sense strand.

(62) A base other than a C at position 17 of the sense strand.

(63) A base other than a C at position 18 of the sense strand.

(64) A G base at position 1 of the sense strand.

(65) A G base at position 2 of the sense strand.

(66) A G base at position 3 of the sense strand.

(67) A base other than a G at position 4 of the sense strand.

(68) A base other than a G at position 5 of the sense strand.

(69) A G base at position 6 of the sense strand.

(70) A G base at position 7 of the sense strand.

(71) A G base at position 8 of the sense strand.

(72) A G base at position 9 of the sense strand.

(73) A base other than a G at position 10 of the sense strand.

(74) A G base at position 11 of the sense strand.

(75) A G base at position 12 of the sense strand.

(76) A G base at position 14 of the sense strand.

(77) A G base at position 15 of the sense strand.

(78) A G base at position 16 of the sense strand.

(79) A base other than a G at position 17 of the sense strand.

(80) A base other than a G at position 18 of the sense strand.

(81) A base other than a G at position 19 of the sense strand.

The importance of various criteria can vary greatly. For instance, a C base at position 10 of the sense strand makes a minor contribution to duplex functionality. In contrast, the absence of a C at position 3 of the sense strand is very important. Accordingly, preferably an siRNA will satisfy as many of the aforementioned criteria as possible.

With respect to the criteria, GC content, as well as a high number of AU in positions 15-19 of the sense strand, may be important for easement of the unwinding of double stranded siRNA duplex. Duplex unwinding has been shown to be crucial for siRNA functionality in vivo.

With respect to criterion 9, the internal structure is measured in terms of the melting temperature of the single strand of siRNA, which is the temperature at which 50% of the molecules will become denatured. With respect to criteria 2-8 and 10-11, the positions refer to sequence positions on the sense strand, which is the strand that is identical to the mRNA.

In one preferred embodiment, at least criteria 1 and 8 are satisfied. In another preferred embodiment, at least criteria 7 and 8 are satisfied. In still another preferred embodiment, at least criteria 1, 8 and 9 are satisfied.

It should be noted that all of the aforementioned criteria regarding sequence position specifics are with respect to the 5′ end of the sense strand. Reference is made to the sense strand, because most databases contain information that describes the information of the mRNA. Because according to the present invention a chain can be from 18 to 30 bases in length, and the aforementioned criteria assumes a chain 19 base pairs in length, it is important to keep the aforementioned criteria applicable to the correct bases.

When there are only 18 bases, the base pair that is not present is the base pair that is located at the 3′ of the sense strand. When there are twenty to thirty bases present, then additional bases are added at the 5′ end of the sense chain and occupy positions ⁻1 to ⁻11. Accordingly, with respect to SEQ. ID NO. 0001 NNANANNNNUCNAANNNNA and SEQ. ID NO. 0028 GUCNNANANNNNUCNAANNNNA, both would have A at position 3, A at position 5, U at position 10, C at position 11, A and position 13, A and position 14 and A at position 19. However, SEQ. ID NO. 0028 would also have C at position −1, U at position −2 and G at position −3.

For a 19 base pair siRNA, an optimal sequence of one of the strands may be represented below, where N is any base, A, C, G, or U: SEQ. ID NO. 0001. NNANANNNNUCNAANNNNA SEQ. ID NO. 0002. NNANANNNNUGNAANNNNA SEQ. ID NO. 0003. NNANANNNNUUNAANNNNA SEQ. ID NO. 0004. NNANANNNNUCNCANNNNA SEQ. ID NO. 0005. NNANANNNNUGNCANNNNA SEQ. ID NO. 0006. NNANANNNNUUNCANNNNA SEQ. ID NO. 0007. NNANANNNNUCNUANNNNA SEQ. ID NO. 0008. NNANANNNNUGNUANNNNA SEQ. ID NO. 0009. NNANANNNNUUNUANNNNA SEQ. ID NO. 0010. NNANCNNNNUCNAANNNNA SEQ. ID NO. 0011. NNANCNNNNUGNAANNNNA SEQ. ID NO. 0012. NNANCNNNNUUNAANNNNA SEQ. ID NO. 0013. NNANCNNNNUCNCANNNNA SEQ. ID NO. 0014. NNANCNNNNUGNCANNNNA SEQ. ID NO. 0015. NNANCNNNNUUNCANNNNA SEQ. ID NO. 0016. NNANCNNNNUCNUANNNNA SEQ. ID NO. 0017. NNANCNNNNUGNUANNNNA SEQ. ID NO. 0018. NNANCNNNNUUNUANNNNA SEQ. ID NO. 0019. NNANGNNNNUCNAANNNNA SEQ. ID NO. 0020. NNANGNNNNUGNAANNNNA SEQ. ID NO. 0021. NNANGNNNNUUNAANNNNA SEQ. ID NO. 0022. NNANGNNNNUCNCANNNNA SEQ. ID NO. 0023. NNANGNNNNUGNCANNNNA SEQ. ID NO. 0024. NNANGNNNNUUNCANNNNA SEQ. ID NO. 0025. NNANGNNNNUCNUANNNNA SEQ. ID NO. 0026. NNANGNNNNUGNUANNNNA SEQ. ID NO. 0027. NNANGNNNNNUNUANNNNA

In one embodiment, the sequence used as an siRNA is selected by choosing the siRNA that score highest according to one of the following seven algorithms that are represented by Formulas I-VI: Relative functionality of siRNA=−(GC/3)+(AU ₁₅₋₁₉)−(Tm _(20°C))*3−(G ₁₃)*3−(C ₁₉)+(A ₁₉)*2+(A ₃)+(U ₁₀)+(A ₁₄)−(U ₅)−(A ₁₁)  Formula I Relative functionality of siRNA=−(GC/3)−(AU ₁₅₋₁₉)*3−(G ₁₃)*3−(C ₁₉)+(A ₁₉)*2+(A ₃)  Formula II Relative functionality of siRNA=−(GC/3)+(AU ₁₅₋₁₉)−(Tm _(20°C))*3  Formula III Relative functionality of siRNA=−GC/2+(AU ₁₅₋₁₉)/2−(Tm _(20°C))*2−(G ₁₃)*3−(C ₁₉)+(A ₁₉)*2+(A ₃)+(U ₁₀)+(A ₁₄)−(U ₅)−(A ₁₁)  Formula IV Relative functionality of siRNA=−(G ₁₃)*3−(C ₁₉)+(A ₁₉)*2+(A ₃)+(U ₁₀)+(A ₁₄)−(U ₅)−(A ₁₁)  Formula V Relative functionality of siRNA=−(G ₁₃)*3−(C ₁₉)+(A ₁₉)*2+(A ₃)  Formula VI Relative functionality of siRNA=−(GC/2)+(AU ₁₅₋₁₉)/2−(Tm _(20°C))*1−(G ₁₃)*3−(C ₁₉)+(A ₁₉)*3+(A ₃)*3+(U ₁₀)/2+(A ₁₄)/2−(U ₅)/2−(A ₁₁)/2  Formula VII

In Formulas I-VII:

wherein A₁₉=1 if A is the base at position 19 on the sense strand, otherwise its value is 0,

AU₁₅₋₁₉=0-5 depending on the number of A or U bases on the sense strand at positions 15-19;

G₁₃=1 if G is the base at position 13 on the sense strand, otherwise its value is 0;

C₁₉=1 if C is the base at position 19 of the sense strand, otherwise its value is 0;

GC=the number of G and C bases in the entire sense strand;

Tm_(20°C)=1 if the Tm is greater than 20° C.;

A₃=1 if A is the base at position 3 on the sense strand, otherwise its value is 0;

U₁₀=1 if U is the base at position 10 on the sense strand, otherwise its value is 0;

A₁₄=1 if A is the base at position 14 on the sense strand, otherwise its value is 0;

U₅=1 if U is the base at position 5 on the sense strand, otherwise its value is 0; and

A₁₁=1 if A is the base at position 11 of the sense strand, otherwise its value is 0.

Formulas I-VII provide relative information regarding functionality. When the values for two sequences are compared for a given formula, the relative functionality is ascertained; a higher positive number indicates a greater functionality. For example, in many applications a value of 5 or greater is beneficial.

Additionally, in many applications, more than one of these formulas would provide useful information as to the relative functionality of potential siRNA sequences. However, it is beneficial to have more than one type of formula, because not every formula will be able to help to differentiate among potential siRNA sequences. For example, in particularly high GC mRNAs, formulas that take that parameter into account would not be useful and application of formulas that lack GC elements (e.g., formulas V and VI) might provide greater insights into duplex functionality. Similarly, formula II might by used in situations where hairpin structures are not observed in duplexes, and formula IV might be applicable for sequences that have higher AU content. Thus, one may consider a particular sequence in light of more than one or even all of these algorithms to obtain the best differentiation among sequences. In some instances, application of a given algorithm may identify an unusually large number of potential siRNA sequences, and in those cases, it may be appropriate to re-analyze that sequence with a second algorithm that is, for instance, more stringent. Alternatively, it is conceivable that analysis of a sequence with a given formula yields no acceptable siRNA sequences (i.e. low SMARTSCORES™, or siRNA ranking). In this instance, it may be appropriate to re-analyze that sequences with a second algorithm that is, for instance, less stringent. In still other instances, analysis of a single sequence with two separate formulas may give rise to conflicting results (i.e. one formula generates a set of siRNA with high SMARTSCORES™, or siRNA ranking, while the other formula identifies a set of siRNA with low SMARTSCORES™, or siRNA ranking). In these instances, it may be necessary to determine which weighted factor(s) (e.g. GC content) are contributing to the discrepancy and assessing the sequence to decide whether these factors should or should not be included. Alternatively, the sequence could be analyzed by a third, fourth, or fifth algorithm to identify a set of rationally designed siRNA.

The above-referenced criteria are particularly advantageous when used in combination with pooling techniques as depicted in Table I: TABLE I FUNCTIONAL PROBABILITY OLIGOS POOLS CRITERIA >95% >80% <70% >95% >80% <70% CURRENT 33.0 50.0 23.0 79.5 97.3 0.3 NEW 50.0 88.5 8.0 93.8 99.98 0.005 (GC) 28.0 58.9 36.0 72.8 97.1 1.6

The term “current” used in Table I refers to Tuschl's conventional siRNA parameters (Elbashir, S. M. et al. (2002) “Analysis of gene function in somatic mammalian cells using small interfering RNAs” Methods 26: 199-213). “New” refers to the design parameters described in Formulas I-VII. “GC” refers to criteria that select siRNA solely on the basis of GC content.

As Table I indicates, when more functional siRNA duplexes are chosen, siRNAs that produce <70% silencing drops from 23% to 8% and the number of siRNA duplexes that produce >80% silencing rises from 50% to 88.5%. Further, of the siRNA duplexes with >80% silencing, a larger portion of these siRNAs actually silence >95% of the target expression (the new criteria increases the portion from 33% to 50%). Using this new criteria in pooled siRNAs, shows that, with pooling, the amount of silencing >95% increases from 79.5% to 93.8% and essentially eliminates any siRNA pool from silencing less than 70%.

Table II similarly shows the particularly beneficial results of pooling in combination with the aforementioned criteria. However, Table II, which takes into account each of the aforementioned variables, demonstrates even a greater degree of improvement in functionality. TABLE II FUNCTIONAL PROBABILITY OLIGOS POOLS NON- NON- FUNCTIONAL AVERAGE FUNCTIONAL FUNCTIONAL AVERAGE FUNCTIONAL RANDOM 20 40 50 67 97 3 CRITERIA 1 52 99 0.1 97 93 0.0040 CRITERIA 4 89 99 0.1 99 99 0.0000

The terms “functional,” “Average,” and “Non-functional” used in Table II, refer to siRNA that exhibit >80%, >50%, and <50% functionality, respectively. Criteria 1 and 4 refer to specific criteria described above.

The above-described algorithms may be used with or without a computer program that allows for the inputting of the sequence of the mRNA and automatically outputs the optimal siRNA. The computer program may, for example, be accessible from a local terminal or personal computer, over an internal network or over the Internet.

In addition to the formulas above, more detailed algorithms, may be used for selecting siRNA. Preferably, at least one RNA duplex of 18-30 base pairs is selected such that it is optimized according a formula selected from: (−14)*G₁₃−13*A₁−12*U₇−11*U₂−10*A₁₁−10*U₄−10*C₃−10*C₅−10*C₆−9*A₁₀− 9*U₉−9*C₁₈−8*G₁₀−7*U₁−7*U₁₆−7*C₁₇−7*C₁₉+7*U₁₇+8*A₂+8*A₄+8*A₅+8*C₄ +9*G₈+10*A₇+10*U₁₈+11*A₁₉+11*C₉+15*G₁+18*A₃+19*U₁₀−Tm−3*(GC_(total)) −6*(GC₁₅₋₁₉)−30*X; and  Formula VIII (14.1)*A₃+(14.9)*A₆+(17.6)*A₁₃+(24.7)*A₁₉+(14.2)*U₁₀+(10.5)* C₉+(23.9)*G₁+(16.3)*G₂+(−12.3)*A₁₁+(−19.3)*U₁+(−12.1)*U₂+(− 11)*U₃+(−15.2)*U₁₅+(−11.3)*U₁₆+(−11.8)*C₃+(−17.4)*C₆+(−10.5)*C₇+ (−13.7)*G₁₃+(−25.9)*G₁₉−Tm−3*(GC_(total))−6*(GC₁₅₋₁₉)−30*X; and  Formula IX (−8)*A1+(−1)*A2+(12)*A3+(7)*A4+(18)*A5+(12)*A6+ (19)*A7+(6)*A8+(−4)*A9+(−5)*A10+(−2)*A11+(−5)*A12+(17)*A13+(− 3)*A14+(4)*A15+(2)*A16+(8)*A17+(11)*A18+(30)*A19+(−13)*U1+(− 10)*U2+(2)*U3+(−2)*U4+(−5)*U5+(5)*U6+(−2)*U7+(−10)*U8+(− 5)*U9+(15)*U10+(−1)*U11+(0)*U12+(10)*U13+(−9)*U14+(−13)*U15+(− 10)*U16+(3)*U17+(9)*U18+(9)*U19+(7)*C1+(3)*C2+(−21)*C3+(5)*C4+(− 9)*C5+(−20)*C6+(−18)*C7+(−5)*C8+(5)*C9+(1)*C10+(2)*C11+(− 5)*C12+(−3)*C13+(−6)*C14+(−2)*C15+(−5)*C16+(−3)*C17+(−12)*C18+(− 18)*C19+(14)*G1+(8)*G2+(7)*G3+(−10)*G4+(− 4)*G5+(2)*G6+(1)*G7+(9)*G8+(5)*G9+(−11)*G10+(1)*G11+(9)*G12+(− 24)*G13+(18)*G14+(11)*G15+(13)*G16+(−7)*G17+(−9)*G18+(−22)*G19+ 6*(number of A+U in position 15-19)−3*(number of G+C in whole siRNA).  Formula X

wherein

A₁=1 if A is the base at position 1 of the sense strand, otherwise its value is 0;

A₂=1 if A is the base at position 2 of the sense strand, otherwise its value is 0;

A₃=1 if A is the base at position 3 of the sense strand, otherwise its value is 0;

A₄=1 if A is the base at position 4 of the sense strand, otherwise its value is 0;

A₅=1 if A is the base at position 5 of the sense strand, otherwise its value is 0;

A₆=1 if A is the base at position 6 of the sense strand, otherwise its value is 0;

A₇=1 if A is the base at position 7 of the sense strand, otherwise its value is 0;

A₁₀=1 if A is the base at position 10 of the sense strand, otherwise its value is 0;

A₁₁=1 if A is the base at position 11 of the sense strand, otherwise its value is 0;

A₁₃=1 if A is the base at position 13 of the sense strand, otherwise its value is 0;

A₁₉=1 if A is the base at position 19 of the sense strand, otherwise if another base is present or the sense strand is only 18 base pairs in length, its value is 0;

C₃=1 if C is the base at position 3 of the sense strand, otherwise its value is 0;

C₄=1 if C is the base at position 4 of the sense strand, otherwise its value is 0;

C₅=1 if C is the base at position 5 of the sense strand, otherwise its value is 0;

C₆=1 if C is the base at position 6 of the sense strand, otherwise its value is 0;

C₇=1 if C is the base at position 7 of the sense strand, otherwise its value is 0;

C₉=1 if C is the base at position 9 of the sense strand, otherwise its value is 0;

C₁₇=1 if C is the base at position 17 of the sense strand, otherwise its value is 0;

C₁₈=1 if C is the base at position 18 of the sense strand, otherwise its value is 0;

C₁₉=1 if C is the base at position 19 of the sense strand, otherwise if another base is present or the sense strand is only 18 base pairs in length, its value is 0;

G₁=1 if G is the base at position 1 on the sense strand, otherwise its value is 0;

G₂=1 if G is the base at position 2 of the sense strand, otherwise its value is 0;

G₈=1 if G is the base at position 8 on the sense strand, otherwise its value is 0;

G₁₀=1 if G is the base at position 10 on the sense strand, otherwise its value is 0;

G₁₃=1 if G is the base at position 13 on the sense strand, otherwise its value is 0;

G₁₉=1 if G is the base at position 19 of the sense strand, otherwise if another base is present or the sense strand is only 18 base pairs in length, its value is 0;

U₁=1 if U is the base at position 1 on the sense strand, otherwise its value is 0;

U₂=1 if U is the base at position 2 on the sense strand, otherwise its value is 0;

U₃=1 if U is the base at position 3 on the sense strand, otherwise its value is 0;

U₄=1 if U is the base at position 4 on the sense strand, otherwise its value is 0;

U₇=1 if U is the base at position 7 on the sense strand, otherwise its value is 0;

U₉=1 if U is the base at position 9 on the sense strand, otherwise its value is 0;

U₁₀=1 if U is the base at position 10 on the sense strand, otherwise its value is 0;

U₁₅=1 if U is the base at position 15 on the sense strand, otherwise its value is 0;

U₁₆=1 if U is the base at position 16 on the sense strand, otherwise its value is 0;

U₁₇=1 if U is the base at position 17 on the sense strand, otherwise its value is 0;

U₁₈=1 if U is the base at position 18 on the sense strand, otherwise its value is 0;

GC₁₅₋₁₉=the number of G and C bases within positions 15-19 of the sense strand, or within positions 15-18 if the sense strand is only 18 base pairs in length;

GC_(total)=the number of G and C bases in the sense strand;

Tm=100 if the siRNA oligo has the internal repeat longer then 4 base pairs, otherwise its value is 0; and

X=the number of times that the same nucleotide repeats four or more times in a row.

The above formulas VIII, IX, and X, as well as formulas I-VII, provide methods for selecting siRNA in order to increase the efficiency of gene silencing. A subset of variables of any of the formulas may be used, though when fewer variables are used, the optimization hierarchy becomes less reliable.

With respect to the variables of the above-referenced formulas, a single letter of A or C or G or U followed by a subscript refers to a binary condition. The binary condition is that either the particular base is present at that particular position (wherein the value is “1”) or the base is not present (wherein the value is “0”). Because position 19 is optional, i.e., there might be only 18 base pairs, when there are only 18 base pairs, any base with a subscript of 19 in the formulas above would have a zero value for that parameter. Before or after each variable is a number followed by *, which indicates that the value of the variable is to be multiplied or weighed by that number.

The numbers preceding the variables A, or G, or C, or U in Formulas VIII, IX, and X (or after the variables in Formula I-VII) were determined by comparing the difference in the frequency of individual bases at different positions in functional siRNA and total siRNA. Specifically, the frequency in which a given base was observed at a particular position in functional groups was compared with the frequency that that same base was observed in the total, randomly selected siRNA set. If the absolute value of the difference between the functional and total values was found to be greater than 6%, that parameter was included in the equation. Thus, for instance, if the frequency of finding a “G” at position 13 (G₁₃) is found to be 6% in a given functional group, and the frequency of G₁₃ in the total population of siRNAs is 20%, the difference between the two values is 6%-20%-14%. As the absolute value is greater than six (6), this factor (−14) is included in the equation. Thus, in Formula VIII, in cases where the siRNA under study has a G in position 13, the accrued value is (−14)*(1)=−14. In contrast, when a base other than G is found at position 13, the accrued value is (−14)*(0)=0.

When developing a means to optimize siRNAs, the inventors observed that a bias toward low internal thermodynamic stability of the duplex at the 5′-antisense (AS) end is characteristic of naturally occurring miRNA precursors. The inventors extended this observation to siRNAs for which functionality had been assessed in tissue culture.

With respect to the parameter GC₁₅₋₁₉, a value of 0-5 will be ascribed depending on the number of G or C bases at positions 15 to 19. If there are only 18 base pairs, the value is between 0 and 4.

With respect to the criterion GC_(total) content, a number from 0-30 will be ascribed, which correlates to the total number of G and C nucleotides on the sense strand, excluding overhangs. Without wishing to be bound by any one theory, it is postulated that the significance of the GC content (as well as AU content at positions 15-19, which is a parameter for formulas III-VII) relates to the easement of the unwinding of a double-stranded siRNA duplex. Duplex unwinding is believed to be crucial for siRNA functionality in vivo and overall low internal stability, especially low internal stability of the first unwound base pair is believed to be important to maintain sufficient processivity of RISC complex-induced duplex unwinding. If the duplex has 19 base pairs, those at positions 15-19 on the sense strand will unwind first if the molecule exhibits a sufficiently low internal stability at that position. As persons skilled in the art are aware, RISC is a complex of approximately twelve proteins; Dicer is one, but not the only, helicase within this complex. Accordingly, although the GC parameters are believed to relate to activity with Dicer, they are also important for activity with other RISC proteins.

The value of the parameter Tm is 0 when there are no internal repeats longer than (or equal to) four base pairs present in the siRNA duplex; otherwise the value is 1. Thus for example, if the sequence ACGUACGU, or any other four nucleotide (or more) palindrome exists within the structure, the value will be one (1). Alternatively if the structure ACGGACG, or any other 3 nucleotide (or less) palindrome exists, the value will be zero (0).

The variable “X” refers to the number of times that the same nucleotide occurs contiguously in a stretch of four or more units. If there are, for example, four contiguous As in one part of the sequence and elsewhere in the sequence four contiguous Cs, X=2. Further, if there are two separate contiguous stretches of four of the same nucleotides or eight or more of the same nucleotides in a row, then X=2. However, X does not increase for five, six or seven contiguous nucleotides.

Again, when applying Formula VIII, Formula IX, or Formula X, to a given mRNA, (the “target RNA” or “target molecule”), one may use a computer program to evaluate the criteria for every sequence of 18-30 base pairs or only sequences of a fixed length, e.g., 19 base pairs. Preferably the computer program is designed such that it provides a report ranking of all of the potential siRNAs 18-30 base pairs, ranked according to which sequences generate the highest value. A higher value refers to a more efficient siRNA for a particular target gene. The computer program that may be used may be developed in any computer language that is known to be useful for scoring nucleotide sequences, or it may be developed with the assistance of commercially available product such as Microsoft's PRODUCT.NET. Additionally, rather than run every sequence through one and/or another formula, one may compare a subset of the sequences, which may be desirable if for example only a subset are available. For instance, it may be desirable to first perform a BLAST (Basic Local Alignment Search Tool) search and to identify sequences that have no homology to other targets. Alternatively, it may be desirable to scan the sequence and to identify regions of moderate GC context, then perform relevant calculations using one of the above-described formulas on these regions. These calculations can be done manually or with the aid of a computer.

As with Formulas I-VII, either Formula VIII, Formula IX, or Formula X may be used for a given mRNA target sequence. However, it is possible that according to one or the other formula more than one siRNA will have the same value. Accordingly, it is beneficial to have a second formula by which to differentiate sequences. Formulas IX and X were derived in a similar fashion as Formula VIII, yet used a larger data set and thus yields sequences with higher statistical correlations to highly functional duplexes. The sequence that has the highest value ascribed to it may be referred to as a “first optimized duplex.” The sequence that has the second highest value ascribed to it may be referred to as a “second optimized duplex.” Similarly, the sequences that have the third and fourth highest values ascribed to them may be referred to as a third optimized duplex and a fourth optimized duplex, respectively. When more than one sequence has the same value, each of them may, for example, be referred to as first optimized duplex sequences or co-first optimized duplexes. Formula X is similar to Formula IX, yet uses a greater numbers of variables and for that reason, identifies sequences on the basis of slightly different criteria.

It should also be noted that the output of a particular algorithm will depend on several of variables including: (1) the size of the data base(s) being analyzed by the algorithm, and (2) the number and stringency of the parameters being applied to screen each sequence. Thus, for example, in U.S. patent application Ser. No. 10/714,333, entitled “Functional and Hyperfunctional siRNA,” filed Nov. 14, 2003, Formula VIII was applied to the known human genome (NCBI REFSEQ database) through ENTREZ (EFETCH). As a result of these procedures, roughly 1.6 million siRNA sequences were identified. Application of Formula VIII to the same database in March of 2004 yielded roughly 2.2 million sequences, a difference of approximately 600,000 sequences resulting from the growth of the database over the course of the months that span this period of time. Application of other formulas (e.g., Formula X) that change the emphasis of, include, or eliminate different variables can yield unequal numbers of siRNAs. Alternatively, in cases where application of one formula to one or more genes fails to yield sufficient numbers of siRNAs with scores that would be indicative of strong silencing, said genes can be reassessed with a second algorithm that is, for instance, less stringent.

siRNA sequences identified using Formula VIII and Formula X (minus sequences generated by Formula VIII) are contained within the sequence listing. The data included in the sequence listing is described more fully below. The sequences identified by Formula VIII and Formula X that are disclosed in the sequence listing may be used in gene silencing applications.

It should be noted that for Formulas VIII, IX, and X all of the aforementioned criteria are identified as positions on the sense strand when oriented in the 5′ to 3′ direction as they are identified in connection with Formulas I-VII unless otherwise specified.

Formulas I-X, may be used to select or to evaluate one, or more than one, siRNA in order to optimize silencing. Preferably, at least two optimized siRNAs that have been selected according to at least one of these formulas are used to silence a gene, more preferably at least three and most preferably at least four. The siRNAs may be used individually or together in a pool or kit. Further, they may be applied to a cell simultaneously or separately. Preferably, the at least two siRNAs are applied simultaneously. Pools are particularly beneficial for many research applications. However, for therapeutics, it may be more desirable to employ a single hyperfunctional siRNA as described elsewhere in this application.

When planning to conduct gene silencing, and it is necessary to choose between two or more siRNAs, one should do so by comparing the relative values when the siRNA are subjected to one of the formulas above. In general a higher scored siRNA should be used.

Useful applications include, but are not limited to, target validation, gene functional analysis, research and drug discovery, gene therapy and therapeutics. Methods for using siRNA in these applications are well known to persons of skill in the art.

Because the ability of siRNA to function is dependent on the sequence of the RNA and not the species into which it is introduced, the present invention is applicable across a broad range of species, including but not limited to all mammalian species, such as humans, dogs, horses, cats, cows, mice, hamsters, chimpanzees and gorillas, as well as other species and organisms such as bacteria, viruses, insects, plants and C. elegans.

The present invention is also applicable for use for silencing a broad range of genes, including but not limited to the roughly 45,000 genes of a human genome, and has particular relevance in cases where those genes are associated with diseases such as diabetes, Alzheimer's, cancer, as well as all genes in the genomes of the aforementioned organisms.

The siRNA selected according to the aforementioned criteria or one of the aforementioned algorithms are also, for example, useful in the simultaneous screening and functional analysis of multiple genes and gene families using high throughput strategies, as well as in direct gene suppression or silencing.

Development of the Algorithms

To identify siRNA sequence features that promote functionality and to quantify the importance of certain currently accepted conventional factors—such as G/C content and target site accessibility—the inventors synthesized an siRNA panel consisting of 270 siRNAs targeting three genes, Human Cyclophilin, Firefly Luciferase, and Human DBI. In all three cases, siRNAs were directed against specific regions of each gene. For Human Cyclophilin and Firefly Luciferase, ninety siRNAs were directed against a 199 bp segment of each respective mRNA. For DBI, 90 siRNAs were directed against a smaller, 109 base pair region of the mRNA. The sequences to which the siRNAs were directed are provided below.

It should be noted that in certain sequences, “t” is present. This is because many databases contain information in this manner. However, the t denotes a uracil residue in in RNA and siRNA. Any algorithm will, unless otherwise specified, process at in a sequence as a u. Human cyclophilin: 193-390, M60857 SEQ. ID NO. 29: gttccaaaaa cagtggataa ttttgtggcc ttagctacag gagagaaagg atttggctac aaaaacagca aattccatcg tgtaatcaag gacttcatga tccagggcgg agacttcacc aggggagatg gcacaggagg aaagagcatc tacggtgagc gcttccccga tgagaacttc aaactgaagc actacgggcc tggctggg Firefly luciferase: 1434-1631, U47298 (pGL3, Promega) SEQ. ID NO. 30: tgaacttccc gccgccgttg ttgttttgga gcacggaaag acgatgacgg aaaaagagat cgtggattac gtcgccagtc aagtaacaac cgcgaaaaag ttgcgcggag gagttgtgtt tgtggacgaa gtaccgaaag gtcttaccgg aaaactcgac gcaagaaaaa tcagagagat cctcataaag gccaagaagg DBI, NM_020548 (202-310) (every position) SEQ. ID NO. 0031: acgggcaagg ccaagtggga tgcctggaat gagctgaaag ggacttccaa ggaagatgcc atgaaagctt acatcaacaa agtagaagag ctaaagaaaa aatacggg

A list of the siRNAs appears in Table III (see Examples Section, Example II)

The set of duplexes was analyzed to identify correlations between siRNA functionality and other biophysical or thermodynamic properties. When the siRNA panel was analyzed in functional and non-functional subgroups, certain nucleotides were much more abundant at certain positions in functional or non-functional groups. More specifically, the frequency of each nucleotide at each position in highly functional siRNA duplexes was compared with that of nonfunctional duplexes in order to assess the preference for or against any given nucleotide at every position. These analyses were used to determine important criteria to be included in the siRNA algorithms (Formulas VIII, IX, and X).

The data set was also analyzed for distinguishing biophysical properties of siRNAs in the functional group, such as optimal percent of GC content, propensity for internal structures and regional thermodynamic stability. Of the presented criteria, several are involved in duplex recognition, RISC activation/duplex unwinding, and target cleavage catalysis.

The original data set that was the source of the statistically derived criteria is shown in FIG. 2. Additionally, this figure shows that random selection yields siRNA duplexes with unpredictable and widely varying silencing potencies as measured in tissue culture using HEK293 cells. In the figure, duplexes are plotted such that each x-axis tick-mark represents an individual siRNA, with each subsequent siRNA differing in target position by two nucleotides for Human Cyclophilin B and Firefly Luciferase, and by one nucleotide for Human DBI. Furthermore, the y-axis denotes the level of target expression remaining after transfection of the duplex into cells and subsequent silencing of the target.

siRNA identified and optimized in this document work equally well in a wide range of cell types. FIG. 3 a shows the evaluation of thirty siRNAs targeting the DBI gene in three cell lines derived from different tissues. Each DBI siRNA displays very similar functionality in HEK293 (ATCC, CRL-1573, human embryonic kidney), HeLa (ATCC, CCL-2, cervical epithelial adenocarcinoma) and DU145 (HTB-81, prostate) cells as determined by the B-DNA assay. Thus, siRNA functionality is determined by the primary sequence of the siRNA and not by the intracellular environment. Additionally, it should be noted that although the present invention provides for a determination of the functionality of siRNA for a given target, the same siRNA may silence more than one gene. For example, the complementary sequence of the silencing siRNA may be present in more than one gene. Accordingly, in these circumstances, it may be desirable not to use the siRNA with highest SMARTSCORE™, or siRNA ranking. In such circumstances, it may be desirable to use the siRNA with the next highest SMARTSCORE™, or siRNA ranking.

To determine the relevance of G/C content in siRNA function, the G/C content of each duplex in the panel was calculated and the functional classes of siRNAs (<F50, ≧F50, ≧F80, ≧F95 where F refers to the percent gene silencing) were sorted accordingly. The majority of the highly-functional siRNAs (≧F95) fell within the G/C content range of 36%-52% (FIG. 3B). Twice as many non-functional (<F50) duplexes fell within the high G/C content groups (>57% GC content) compared to the 36%-52% group. The group with extremely low GC content (26% or less) contained a higher proportion of non-functional siRNAs and no highly-functional siRNAs. The G/C content range of 30%-52% was therefore selected as Criterion I for siRNA functionality, consistent with the observation that a G/C range 30%-70% promotes efficient RNAi targeting. Application of this criterion alone provided only a marginal increase in the probability of selecting functional siRNAs from the panel: selection of F50 and F95 siRNAs was improved by 3.6% and 2.2%, respectively. The siRNA panel presented here permitted a more systematic analysis and quantification of the importance of this criterion than that used previously.

A relative measure of local internal stability is the A/U base pair (bp) content; therefore, the frequency of A/U bp was determined for each of the five terminal positions of the duplex (5′ sense (S)/5′ antisense (AS)) of all siRNAs in the panel. Duplexes were then categorized by the number of A/U bp in positions 1-5 and 15-19 of the sense strand. The thermodynamic flexibility of the duplex 5′-end (positions 1-5; S) did not appear to correlate appreciably with silencing potency, while that of the 3′-end (positions 15-19; S) correlated with efficient silencing. No duplexes lacking A/U bp in positions 15-19 were functional. The presence of one A/U bp in this region conferred some degree of functionality, but the presence of three or more A/Us was preferable and therefore defined as Criterion II. When applied to the test panel, only a marginal increase in the probability of functional siRNA selection was achieved: a 1.8% and 2.3% increase for F50 and F95 duplexes, respectively (Table IV).

The complementary strands of siRNAs that contain internal repeats or palindromes may form internal fold-back structures. These hairpin-like structures exist in equilibrium with the duplexed form effectively reducing the concentration of functional duplexes. The propensity to form internal hairpins and their relative stability can be estimated by predicted melting temperatures. High Tm reflects a tendency to form hairpin structures. Lower Tm values indicate a lesser tendency to form hairpins. When the functional classes of siRNAs were sorted by T_(m) (FIG. 3 c), the following trends were identified: duplexes lacking stable internal repeats were the most potent silencers (no F95 duplex with predicted hairpin structure T_(m)>60° C.). In contrast, about 60% of the duplexes in the groups having internal hairpins with calculated T_(m) values less than 20° C. were F80. Thus, the stability of internal repeats is inversely proportional to the silencing effect and defines Criterion III (predicted hairpin structure T_(m)≦20° C.).

Sequence-Based Determinants of siRNA Functionality

When the siRNA panel was sorted into functional and non-functional groups, the frequency of a specific nucleotide at each position in a functional siRNA duplex was compared with that of a nonfunctional duplex in order to assess the preference for or against a certain nucleotide. FIG. 4 shows the results of these queries and the subsequent resorting of the data set (from FIG. 2). The data is separated into two sets: those duplexes that meet the criteria, a specific nucleotide in a certain position—grouped on the left (Selected) and those that do not—grouped on the right (Eliminated). The duplexes are further sorted from most functional to least functional with the y-axis of FIG. 4 a-e representing the % expression i.e., the amount of silencing that is elicited by the duplex (Note: each position on the X-axis represents a different duplex). Statistical analysis revealed correlations between silencing and several sequence-related properties of siRNAs. FIG. 4 and Table IV show quantitative analysis for the following five sequence-related properties of siRNA: (A) an A at position 19 of the sense strand; (B) an A at position 3 of the sense strand; (C) a U at position 10 of the sense strand; (D) a base other than G at position 13 of the sense strand; and (E) a base other than C at position 19 of the sense strand.

When the siRNAs in the panel were evaluated for the presence of an A at position 19 of the sense strand, the percentage of non-functional duplexes decreased from 20% to 11.8%, and the percentage of F95 duplexes increased from 21.7% to 29.4% (Table IV). Thus, the presence of an A in this position defined Criterion IV.

Another sequence-related property correlated with silencing was the presence of an A in position 3 of the sense strand (FIG. 4 b). Of the siRNAs with A3, 34.4% were F95, compared with 21.7% randomly selected siRNAs. The presence of a U base in position 10 of the sense strand exhibited an even greater impact (FIG. 4 c). Of the duplexes in this group, 41.7% were F95. These properties became criteria V and VI, respectively.

Two negative sequence-related criteria that were identified also appear on FIG. 4. The absence of a G at position 13 of the sense strand, conferred a marginal increase in selecting functional duplexes (FIG. 4 d). Similarly, lack of a C at position 19 of the sense strand also correlated with functionality (FIG. 4 e). Thus, among functional duplexes, position 19 was most likely occupied by A, and rarely occupied by C. These rules were defined as criteria VII and VIII, respectively.

Application of each criterion individually provided marginal but statistically significant increases in the probability of selecting a potent siRNA. Although the results were informative, the inventors sought to maximize potency and therefore consider multiple criteria or parameters. Optimization is particularly important when developing therapeutics. Interestingly, the probability of selecting a functional siRNA based on each thermodynamic criteria was 2%-4% higher than random, but 4%-8% higher for the sequence-related determinates. Presumably, these sequence-related increases reflect the complexity of the RNAi mechanism and the multitude of protein-RNA interactions that are involved in RNAi-mediated silencing. TABLE IV IMPROVEMENT PERCENT OVER CRITERION FUNCTIONAL RANDOM (%) I. 30%-52% G/C Content <F50 16.4 −3.6 ≧F50 83.6 3.6 ≧F80 60.4 4.3 ≧F95 23.9 2.2 II. At least 3 A/U <F50 18.2 −1.8 bases at positions ≧F50 81.8 1.8 15-19 of the sense ≧F80 59.7 3.6 strand ≧F95 24.0 2.3 III. Absence of internal <F50 16.7 −3.3 repeats, as measured ≧F50 83.3 3.3 by Tm of secondary ≧F80 61.1 5.0 structure ≦20° C. ≧F95 24.6 2.9 IV. An A base at <F50 11.8 −8.2 position 19 ≧F50 88.2 8.2 of the sense strand ≧F80 75.0 18.9 ≧F95 29.4 7.7 V. An A base at <F50 17.2 −2.8 position 3 of ≧F50 82.8 2.8 the sense strand ≧F80 62.5 6.4 ≧F95 34.4 12.7 VI. A U base at <F50 13.9 −6.1 position 10 of ≧F50 86.1 6.1 the sense strand ≧F80 69.4 13.3 ≧F95 41.7 20 VII. A base other than <F50 18.8 −1.2 C at position 19 ≧F50 81.2 1.2 of the sense strand ≧F80 59.7 3.6 ≧F95 24.2 2.5 VIII. A base other than <F50 15.2 −4.8 G at position 13 ≧F50 84.8 4.8 of the sense strand ≧F80 61.4 5.3 ≧F95 26.5 4.8 The siRNA Selection Algorithm

In an effort to improve selection further, all identified criteria, including but not limited to those listed in Table IV were combined into the algorithms embodied in Formula VIII, Formula IX, and Formula X. Each siRNA was then assigned a score (referred to as a SMARTSCORE™, or siRNA ranking) according to the values derived from the formulas. Duplexes that scored higher than 0 or −20 (unadjusted), for Formulas VIII and IX, respectively, effectively selected a set of functional siRNAs and excluded all non-functional siRNAs. Conversely, all duplexes scoring lower than 0 and −20 (minus 20) according to formulas VIII and IX, respectively, contained some functional siRNAs but included all non-functional siRNAs. A graphical representation of this selection is shown in FIG. 5. It should be noted that the scores derived from the algorithm can also be provided as “adjusted” scores. To convert Formula VIII unadjusted scores into adjusted scores it is necessary to use the following equation: (160+unadjusted score)/2.25

When this takes place, an unadjusted score of “0” (zero) is converted to 75. Similarly, unadjusted scores for Formula X can be converted to adjusted scores. In this instance, the following equation is applied: (228+unadjusted score)/3.56

When these manipulations take place, an unadjusted score of 38 is converted to an adjusted score of 75.

The methods for obtaining the seven criteria embodied in Table IV are illustrative of the results of the process used to develop the information for Formulas VIII, IX, and X. Thus similar techniques were used to establish the other variables and their multipliers. As described above, basic statistical methods were use to determine the relative values for these multipliers.

To determine the value for “Improvement over Random” the difference in the frequency of a given attribute (e.g., GC content, base preference) at a particular position is determined between individual functional groups (e.g., <F50) and the total siRNA population studied (e.g., 270 siRNA molecules selected randomly). Thus, for instance, in Criterion I (30%-52% GC content) members of the <F50 group were observed to have GC contents between 30-52% in 16.4% of the cases. In contrast, the total group of 270 siRNAs had GC contents in this range, 20% of the time. Thus for this particular attribute, there is a small negative correlation between 30%-52% GC content and this functional group (i.e., 16.4%-20%=−3.6%). Similarly, for Criterion VI, (a “U” at position 10 of the sense strand), the >F95 group contained a “U” at this position 41.7% of the time. In contrast, the total group of 270 siRNAs had a “U” at this position 21.7% of the time, thus the improvement over random is calculated to be 20% (or 41.7%-21.7%).

Identifying the Average Internal Stability Profile of Strong siRNA

In order to identify an internal stability profile that is characteristic of strong siRNA, 270 different siRNAs derived from the cyclophilin B, the diazepam binding inhibitor (DBI), and the luciferase gene were individually transfected into HEK293 cells and tested for their ability to induce RNAi of the respective gene. Based on their performance in the in vivo assay, the sequences were then subdivided into three groups, (i) >95% silencing; (ii) 80-95% silencing; and (iii) less than 50% silencing. Sequences exhibiting 51-84% silencing were eliminated from further consideration to reduce the difficulties in identifying relevant thermodynamic patterns.

Following the division of siRNA into three groups, a statistical analysis was performed on each member of each group to determine the average internal stability profile (AISP) of the siRNA. To accomplish this the Oligo 5.0 Primer Analysis Software and other related statistical packages (e.g., Excel) were exploited to determine the internal stability of pentamers using the nearest neighbor method described by Freier et al., (1986) Improved free-energy parameters for predictions of RNA duplex stability, Proc Natl. Acad. Sci. USA 83(24): 9373-7. Values for each group at each position were then averaged, and the resulting data were graphed on a linear coordinate system with the Y-axis expressing the ΔG (free energy) values in kcal/mole and the X-axis identifying the position of the base relative to the 5′ end.

The results of the analysis identified multiple key regions in siRNA molecules that were critical for successful gene silencing. At the 3′-most end of the sense strand (5′antisense), highly functional siRNA (>95% gene silencing, see FIG. 6 a, >F95) have a low internal stability (AISP of position 19=˜−7.6 kcal/mol). In contrast low-efficiency siRNA (i.e., those exhibiting less than 50% silencing, <F50) display a distinctly different profile, having high ΔG values (˜−8.4 kcal/mol) for the same position. Moving in a 5′ (sense strand) direction, the internal stability of highly efficient siRNA rises (position 12=˜−8.3 kcal/mole) and then drops again (position 7=˜−7.7 kcal/mol) before leveling off at a value of approximately −8.1 kcal/mol for the 5′ terminus. siRNA with poor silencing capabilities show a distinctly different profile. While the AISP value at position 12 is nearly identical with that of strong siRNAs, the values at positions 7 and 8 rise considerably, peaking at a high of ˜−9.0 kcal/mol. In addition, at the 5′ end of the molecule the AISP profile of strong and weak siRNA differ dramatically. Unlike the relatively strong values exhibited by siRNA in the >95% silencing group, siRNAs that exhibit poor silencing activity have weak AISP values (−7.6, −7.5, and −7.5 kcal/mol for positions 1, 2 and 3 respectively).

Overall the profiles of both strong and weak siRNAs form distinct sinusoidal shapes that are roughly 180° out-of-phase with each other. While these thermodynamic descriptions define the archetypal profile of a strong siRNA, it will likely be the case that neither the ΔG values given for key positions in the profile or the absolute position of the profile along the Y-axis (i.e., the ΔG-axis) are absolutes. Profiles that are shifted upward or downward (i.e., having on an average, higher or lower values at every position) but retain the relative shape and position of the profile along the X-axis can be foreseen as being equally effective as the model profile described here. Moreover, it is likely that siRNA that have strong or even stronger gene-specific silencing effects might have exaggerated ΔG values (either higher or lower) at key positions. Thus, for instance, it is possible that the 5′-most position of the sense strand (position 19) could have ΔG values of 7.4 kcal/mol or lower and still be a strong siRNA if, for instance, a G-C→G-T/U mismatch were substituted at position 19 and altered duplex stability. Similarly, position 12 and position 7 could have values above 8.3 kcal/mol and below 7.7 kcal/mole, respectively, without abating the silencing effectiveness of the molecule. Thus, for instance, at position 12, a stabilizing chemical modification (e.g., a chemical modification of the 2′ position of the sugar backbone) could be added that increases the average internal stability at that position. Similarly, at position 7, mismatches similar to those described previously could be introduced that would lower the ΔG values at that position.

Lastly, it is important to note that while functional and non-functional siRNA were originally defined as those molecules having specific silencing properties, both broader or more limiting parameters can be used to define these molecules. As used herein, unless otherwise specified, “non-functional siRNA” are defined as those siRNA that induce less than 50% (<50%) target silencing, “semi-functional siRNA” induce 50-79% target silencing, “functional siRNA” are molecules that induce 80-95% gene silencing, and “highly-functional siRNA” are molecules that induce great than 95% gene silencing. These definitions are not intended to be rigid and can vary depending upon the design and needs of the application. For instance, it is possible that a researcher attempting to map a gene to a chromosome using a functional assay, may identify an siRNA that reduces gene activity by only 30%. While this level of gene silencing may be “non-functional” for, e.g., therapeutic needs, it is sufficient for gene mapping purposes and is, under these uses and conditions, “functional.” For these reasons, functional siRNA can be defined as those molecules having greater than 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% silencing capabilities at 100 nM transfection conditions. Similarly, depending upon the needs of the study and/or application, non-functional and semi-functional siRNA can be defined as having different parameters. For instance, semi-functional siRNA can be defined as being those molecules that induce 20%, 30%, 40%, 50%, 60%, or 70% silencing at 100 nM transfection conditions. Similarly, non-functional siRNA can be defined as being those molecules that silence gene expression by less than 70%, 60%, 50%, 40%, 30%, or less. Nonetheless, unless otherwise stated, the descriptions stated in the “Definitions” section of this text should be applied.

Functional attributes can be assigned to each of the key positions in the AISP of strong siRNA. The low 5′ (sense strand) AISP values of strong siRNAs may be necessary for determining which end of the molecule enters the RISC complex. In contrast, the high and low AISP values observed in the central regions of the molecule may be critical for siRNA-target mRNA interactions and product release, respectively.

If the AISP values described above accurately define the thermodynamic parameters of strong siRNA, it would be expected that similar patterns would be observed in strong siRNA isolated from nature. Natural siRNAs exist in a harsh, RNase-rich environment and it can be hypothesized that only those siRNA that exhibit heightened affinity for RISC (i.e., siRNA that exhibit an average internal stability profile similar to those observed in strong siRNA) would survive in an intracellular environment. This hypothesis was tested using GFP-specific siRNA isolated from N. benthamiana. Llave et al. (2002) Endogenous and Silencing-Associated Small RNAs in Plants, The Plant Cell 14, 1605-1619, introduced long double-stranded GFP-encoding RNA into plants and subsequently re-isolated GFP-specific siRNA from the tissues. The AISP of fifty-nine of these GFP-siRNA were determined, averaged, and subsequently plotted alongside the AISP profile obtained from the cyclophilin B/DBI/luciferase siRNA having >90% silencing properties (FIG. 6 b). Comparison of the two groups show that profiles are nearly identical. This finding validates the information provided by the internal stability profiles and demonstrates that: (1) the profile identified by analysis of the cyclophilin B/DBI/luciferase siRNAs are not gene specific; and (2) AISP values can be used to search for strong siRNAs in a variety of species.

Both chemical modifications and base-pair mismatches can be incorporated into siRNA to alter the duplex's AISP and functionality. For instance, introduction of mismatches at positions 1 or 2 of the sense strand destabilized the 5′ end of the sense strand and increases the functionality of the molecule (see Luc, FIG. 7). Similarly, addition of 2′-O-methyl groups to positions 1 and 2 of the sense strand can also alter the AISP and (as a result) increase both the functionality of the molecule and eliminate off-target effects that results from sense strand homology with the unrelated targets (FIG. 8).

Rationale for Criteria in a Biological Context

The fate of siRNA in the RNAi pathway may be described in 5 major steps: (1) duplex recognition and pre-RISC complex formation; (2) ATP-dependent duplex unwinding/strand selection and RISC activation; (3) mRNA target identification; (4) mRNA cleavage, and (5) product release (FIG. 1). Given the level of nucleic acid-protein interactions at each step, siRNA functionality is likely influenced by specific biophysical and molecular properties that promote efficient interactions within the context of the multi-component complexes. Indeed, the systematic analysis of the siRNA test set identified multiple factors that correlate well with functionality. When combined into a single algorithm, they proved to be very effective in selecting active siRNAs.

The factors described here may also be predictive of key functional associations important for each step in RNAi. For example, the potential formation of internal hairpin structures correlated negatively with siRNA functionality. Complementary strands with stable internal repeats are more likely to exist as stable hairpins thus decreasing the effective concentration of the functional duplex form. This suggests that the duplex is the preferred conformation for initial pre-RISC association. Indeed, although single complementary strands can induce gene silencing, the effective concentration required is at least two orders of magnitude higher than that of the duplex form.

siRNA-pre-RISC complex formation is followed by an ATP-dependent duplex unwinding step and “activation” of the RISC. The siRNA functionality was shown to correlate with overall low internal stability of the duplex and low internal stability of the 3′ sense end (or differential internal stability of the 3′ sense compare to the 5′ sense strand), which may reflect strand selection and entry into the RISC. Overall duplex stability and low internal stability at the 3′ end of the sense strand were also correlated with siRNA functionality. Interestingly, siRNAs with very high and very low overall stability profiles correlate strongly with non-functional duplexes. One interpretation is that high internal stability prevents efficient unwinding while very low stability reduces siRNA target affinity and subsequent mRNA cleavage by the RISC.

Several criteria describe base preferences at specific positions of the sense strand and are even more intriguing when considering their potential mechanistic roles in target recognition and mRNA cleavage. Base preferences for A at position 19 of the sense strand but not C, are particularly interesting because they reflect the same base preferences observed for naturally occurring miRNA precursors. That is, among the reported miRNA precursor sequences 75% contain a U at position 1 which corresponds to an A in position 19 of the sense strand of siRNAs, while G was under-represented in this same position for miRNA precursors. These observations support the hypothesis that both miRNA precursors and siRNA duplexes are processed by very similar if not identical protein machinery. The functional interpretation of the predominance of a U/A base pair is that it promotes flexibility at the 5′antisense ends of both siRNA duplexes and miRNA precursors and facilitates efficient unwinding and selective strand entrance into an activated RISC.

Among the criteria associated with base preferences that are likely to influence mRNA cleavage or possibly product release, the preference for U at position 10 of the sense strand exhibited the greatest impact, enhancing the probability of selecting an F80 sequence by 13.3%. Activated RISC preferentially cleaves target mRNA between nucleotides 10 and 11 relative to the 5′ end of the complementary targeting strand. Therefore, it may be that U, the preferred base for most endoribonucleases, at this position supports more efficient cleavage. Alternatively, a U/A bp between the targeting siRNA strand and its cognate target in RNA may create an optimal conformation for the RISC-associated “slicing” activity.

Post Algorithm Filters

According to another embodiment, the output of any one of the formulas previously listed can be filtered to remove or select for siRNAs containing undesirable or desirable motifs or properties, respectively. In one example, sequences identified by any of the formulas can be filtered to remove any and all sequences that induce toxicity or cellular stress. Introduction of an siRNA containing a toxic motif into a cell can induce cellular stress and/or cell death (apoptosis) which in turn can mislead researchers into associating a particular (e.g., nonessential) gene with, e.g., an essential function. Alternatively, sequences generated by any of the before mentioned formulas can be filtered to identify and retain duplexes that contain toxic motifs. Such duplexes may be valuable from a variety of perspectives including, for instance, uses as therapeutic molecules. A variety of toxic motifs exist and can exert their influence on the cell through RNAi and non-RNAi pathways. Examples of toxic motifs are explained more fully in commonly assigned U.S. Provisional Patent Application Ser. No. 60/538,874, entitled “Identification of Toxic Sequences,” filed Jan. 23, 2004. Briefly, toxic: motifs include A/G UUU A/G/U, G/C AAA G/C, and GCCA, or a complement of any of the foregoing.

In another instance, sequences identified by any of the before mentioned formulas can be filtered to identify duplexes that contain motifs (or general properties) that provide serum stability or induce serum instability. In one envisioned application of siRNA as therapeutic molecules, duplexes targeting disease-associated genes will be introduced into patients intravenously. As the half-life of single and double stranded RNA in serum is short, post-algorithm filters designed to select molecules that contain motifs that enhance duplex stability in the presence of serum and/or (conversely) eliminate duplexes that contain motifs that destabilize siRNA in the presence of serum, would be beneficial.

In another instance, sequences identified by any of the before mentioned formulas can be filtered to identify duplexes that are hyperfunctional. Hyperfunctional sequences are defined as those sequences that (1) induce greater than 95% silencing of a specific target when they are transfected at subnanomolar concentrations (i.e., less than one nanomolar); and/or (2) induce functional (or better) levels of silencing for greater than 96 hours. Filters that identify hyperfunctional molecules can vary widely. In one example, the top ten, twenty, thirty, or forty siRNA can be assessed for the ability to silence a given target at, e.g., concentrations of 1 nM and 0.5 nM to identify hyperfunctional molecules.

Pooling

According to another embodiment, the present invention provides a pool of at least two siRNAs, preferably in the form of a kit or therapeutic reagent, wherein one strand of each of the siRNAs, the sense strand comprises a sequence that is substantially similar to a sequence within a target mRNA. The opposite strand, the antisense strand, will preferably comprise a sequence that is substantially complementary to that of the target mRNA. More preferably, one strand of each siRNA will comprise a sequence that is identical to a sequence that is contained in the target mRNA. Most preferably, each siRNA will be 19 base pairs in length, and one strand of each of the siRNAs will be 100% complementary to a portion of the target mRNA.

By increasing the number of siRNAs directed to a particular target using a pool or kit, one is able both to increase the likelihood that at least one siRNA with satisfactory functionality will be included, as well as to benefit from additive or synergistic effects. Further, when two or more siRNAs directed against a single gene do not have satisfactory levels of functionality alone, if combined, they may satisfactorily promote degradation of the target messenger RNA and successfully inhibit translation. By including multiple siRNAs in the system, not only is the probability of silencing increased, but the economics of operation are also improved when compared to adding different siRNAs sequentially. This effect is contrary to the conventional wisdom that the concurrent use of multiple siRNA will negatively impact gene silencing (e.g., Holen, T. et al. (2003) Similar behavior of single strand and double strand siRNAs suggests they act through a common RNAi pathway. NAR 31: 2401-21407).

In fact, when two siRNAs were pooled together, 54% of the pools of two siRNAs induced more than 95% gene silencing. Thus, a 2.5-fold increase in the percentage of functionality was achieved by randomly combining two siRNAs. Further, over 84% of pools containing two siRNAs induced more than 80% gene silencing.

More preferably, the kit is comprised of at least three siRNAs, wherein one strand of each siRNA comprises a sequence that is substantially similar to a sequence of the target mRNA and the other strand comprises a sequence that is substantially complementary to the region of the target mRNA. As with the kit that comprises at least two siRNAs, more preferably one strand will comprise a sequence that is identical to a sequence that is contained in the mRNA and another strand that is 100% complementary to a sequence that is contained in the mRNA. During experiments, when three siRNAs were combined together, 60% of the pools induced more than 95% gene silencing and 92% of the pools induced more than 80% gene silencing.

Further, even more preferably, the kit is comprised of at least four siRNAs, wherein one strand of each siRNA comprises a sequence that is substantially similar to a region of the sequence of the target mRNA, and the other strand comprises a sequence that is substantially complementary to the region of the target mRNA. As with the kit or pool that comprises at least two siRNAs, more preferably one strand of each of the siRNA duplexes will comprise a sequence that is identical to a sequence that is contained in the mRNA, and another strand that is 100% complementary to a sequence that is contained in the mRNA.

Additionally, kits and pools with at least five, at least six, and at least seven siRNAs may also be useful with the present invention. For example, pools of five siRNA induced 95% gene silencing with 77% probability and 80% silencing with 98.8% probability. Thus, pooling of siRNAs together can result in the creation of a target-specific silencing reagent with almost a 99% probability of being functional. The fact that such high levels of success are achievable using such pools of siRNA, enables one to dispense with costly and time-consuming target-specific validation procedures.

For this embodiment, as well as the other aforementioned embodiments, each of the siRNAs within a pool will preferably comprise 18-30 base pairs, more preferably 18-25 base pairs, and most preferably 19 base pairs. Within each siRNA, preferably at least 18 contiguous bases of the antisense strand will be 100% complementary to the target mRNA. More preferably, at least 19 contiguous bases of the antisense strand will be 100% complementary to the target mRNA. Additionally, there may be overhangs on either the sense strand or the antisense strand, and these overhangs may be at either the 5′ end or the 3′ end of either of the strands, for example there may be one or more overhangs of 1-6 bases. When overhangs are present, they are not included in the calculation of the number of base pairs. The two nucleotide 3′ overhangs mimic natural siRNAs and are commonly used but are not essential. Preferably, the overhangs should consist of two nucleotides, most often dTdT or UU at the 3′ end of the sense and antisense strand that are not complementary to the target sequence. The siRNAs may be produced by any method that is now known or that comes to be known for synthesizing double stranded RNA that one skilled in the art would appreciate would be useful in the present invention. Preferably, the siRNAs will be produced by Dharmacon's proprietary ACE® technology. However, other methods for synthesizing siRNAs are well known to persons skilled in the art and include, but are not limited to, any chemical synthesis of RNA oligonucleotides, ligation of shorter oligonucleotides, in vitro transcription of RNA oligonucleotides, the use of vectors for expression within cells, recombinant Dicer products and PCR products.

The siRNA duplexes within the aforementioned pools of siRNAs may correspond to overlapping sequences within a particular mRNA, or non-overlapping sequences of the mRNA. However, preferably they correspond to non-overlapping sequences. Further, each siRNA may be selected randomly, or one or more of the siRNA may be selected according to the criteria discussed above for maximizing the effectiveness of siRNA.

Included in the definition of siRNAs are siRNAs that contain substituted and/or labeled nucleotides that may, for example, be labeled by radioactivity, fluorescence or mass. The most common substitutions are at the 2′ position of the ribose sugar, where moieties such as H (hydrogen) F, NH₃, OCH₃ and other O— alkyl, alkenyl, alkynyl, and orthoesters, may be substituted, or in the phosphorous backbone, where sulfur, amines or hydrocarbons may be substituted for the bridging of non-bridging atoms in the phosphodiester bond. Examples of modified siRNAs are explained more fully in commonly assigned U.S. patent application Ser. No. 10/613,077, filed Jul. 1, 2003.

Additionally, as noted above, the cell type into which the siRNA is introduced may affect the ability of the siRNA to enter the cell; however, it does not appear to affect the ability of the siRNA to function once it enters the cell. Methods for introducing double-stranded RNA into various cell types are well known to persons skilled in the art.

As persons skilled in the art are aware, in certain species, the presence of proteins such as RdRP, the RNA-dependent RNA polymerase, may catalytically enhance the activity of the siRNA. For example, RdRP propagates the RNAi effect in C. elegans and other non-mammalian organisms. In fact, in organisms that contain these proteins, the siRNA may be inherited. Two other proteins that are well studied and known to be a part of the machinery are members of the Argonaute family and Dicer, as well as their homologues. There is also initial evidence that the RISC complex might be associated with the ribosome so the more efficiently translated mRNAs will be more susceptible to silencing than others.

Another very important factor in the efficacy of siRNA is mRNA localization. In general, only cytoplasmic in RNAs are considered to be accessible to RNAi to any appreciable degree. However, appropriately designed siRNAs, for example, siRNAs modified with internucleotide linkages or 2′-O-methyl groups, may be able to cause silencing by acting in the nucleus. Examples of these types of modifications are described in commonly assigned U.S. patent application Ser. Nos. 10/431,027 and 10/613,077.

As described above, even when one selects at least two siRNAs at random, the effectiveness of the two may be greater than one would predict based on the effectiveness of two individual siRNAs. This additive or synergistic effect is particularly noticeable as one increases to at least three siRNAs, and even more noticeable as one moves to at least four siRNAs. Surprisingly, the pooling of the non-functional and semi-functional siRNAs, particularly more than five siRNAs, can lead to a silencing mixture that is as effective if not more effective than any one particular functional siRNA.

Within the kits of the present invention, preferably each siRNA will be present in a concentration of between 0.001 and 200 μM, more preferably between 0.01 and 200 nM, and most preferably between 0.1 and 10 nM.

In addition to preferably comprising at least four or five siRNAs, the kits of the present invention will also preferably comprise a buffer to keep the siRNA duplex stable. Persons skilled in the art are aware of buffers suitable for keeping siRNA stable. For example, the buffer may be comprised of 100 mM KCl, 30 mM HEPES-pH 7.5, and 1 mM MgCl₂. Alternatively, kits might contain complementary strands that contain any one of a number of chemical modifications (e.g., a 2′-O-ACE) that protect the agents from degradation by nucleases. In this instance, the user may (or may not) remove the modifying protective group (e.g., deprotect) before annealing the two complementary strands together.

By way of example, the kits may be organized such that pools of siRNA duplexes are provided on an array or microarray of wells or drops for a particular gene set or for unrelated genes. The array may, for example, be in 96 wells, 384 wells or 1284 wells arrayed in a plastic plate or on a glass slide using techniques now known or that come to be known to persons skilled in the art. Within an array, preferably there will be controls such as functional anti-lamin A/C, cyclophilin and two siRNA duplexes that are not specific to the gene of interest.

In order to ensure stability of the siRNA pools prior to usage, they may be retained in lyophilized form at minus twenty degrees (−20° C.) until they are ready for use. Prior to usage, they should be resuspended; however, even once resuspended, for example, in the aforementioned buffer, they should be kept at minus twenty degrees, (−20° C.) until used. The aforementioned buffer, prior to use, may be stored at approximately 4° C. or room temperature. Effective temperatures at which to conduct transfections are well known to persons skilled in the art and include for example, room temperature.

The kits may be applied either in vivo or in vitro. Preferably, the siRNA of the pools or kits is applied to a cell through transfection, employing standard transfection protocols. These methods are well known to persons skilled in the art and include the use of lipid-based carriers, electroporation, cationic carriers, and microinjection. Further, one could apply the present invention by synthesizing equivalent DNA sequences (either as two separate, complementary strands, or as hairpin molecules) instead of siRNA sequences and introducing them into cells through vectors. Once in the cells, the cloned DNA could be transcribed, thereby forcing the cells to generate the siRNA. Examples of vectors suitable for use with the present application include but are not limited to the standard transient expression vectors, adenoviruses, retroviruses, lentivirus-based vectors, as well as other traditional expression vectors. Any vector that has an adequate siRNA expression and procession module may be used. Furthermore, certain chemical modifications to siRNAs, including but not limited to conjugations to other molecules, may be used to facilitate delivery. For certain applications it may be preferable to deliver molecules without transfection by simply formulating in a physiological acceptable solution.

This embodiment may be used in connection with any of the aforementioned embodiments. Accordingly, the sequences within any pool may be selected by rational design.

Multigene Silencing

In addition to developing kits that contain multiple siRNA directed against a single gene, another embodiment includes the use of multiple siRNA targeting multiple genes. Multiple genes may be targeted through the use of high- or hyper-functional siRNA. High- or hyper-functional siRNA that exhibit increased potency, require lower concentrations to induce desired phenotypic (and thus therapeutic) effects. This circumvents RISC saturation. It therefore reasons that if lower concentrations of a single siRNA are needed for knockout or knockdown expression of one gene, then the remaining (uncomplexed) RISC will be free and available to interact with siRNA directed against two, three, four, or more, genes. Thus in this embodiment, the authors describe the use of highly functional or hyper-functional siRNA to knock out three separate genes. More preferably, such reagents could be combined to knockout four distinct genes. Even more preferably, highly functional or hyperfunctional siRNA could be used to knock out five distinct genes. Most preferably, siRNA of this type could be used to knockout or knockdown the expression of six or more genes.

Hyperfunctional siRNA

The term hyperfunctional siRNA (hf-siRNA) describes a subset of the siRNA population that induces RNAi in cells at low- or sub-nanomolar concentrations for extended periods of time. These traits, heightened potency and extended longevity of the RNAi phenotype, are highly attractive from a therapeutic standpoint. Agents having higher potency require lesser amounts of the molecule to achieve the desired physiological response, thus reducing the probability of side effects due to “off-target” interference. In addition to the potential therapeutic benefits associated with hyperfunctional siRNA, hf-siRNA are also desirable from an economic perspective. Hyperfunctional siRNA may cost less on a per-treatment basis, thus reducing overall expenditures to both the manufacturer and the consumer.

Identification of hyperfunctional siRNA involves multiple steps that are designed to examine an individual siRNA agent's concentration- and/or longevity-profiles. In one non-limiting example, a population of siRNA directed against a single gene are first analyzed using the previously described algorithm (Formula VIII). Individual siRNA are then introduced into a test cell line and assessed for the ability to degrade the target mRNA. It is important to note that when performing this step it is not necessary to test all of the siRNA. Instead, it is sufficient to test only those siRNA having the highest SMARTSCORES™, or siRNA ranking (i.e., SMARTSCORES™M, or siRNA ranking >−10). Subsequently, the gene silencing data is plotted against the SMARTSCORES™, or siRNA rankings (see FIG. 9). siRNA that (1) induce a high degree of gene silencing (i.e., they induce greater than 80% gene knockdown) and (2) have superior SMARTSCORES™ (i.e., a SMARTSCORE™, or siRNA ranking, of >−10, suggesting a desirable average internal stability profile) are selected for further investigations designed to better understand the molecule's potency and longevity. In one, non-limiting study dedicated to understanding a molecule's potency, an siRNA is introduced into one (or more) cell types in increasingly diminishing concentrations (e.g., 3.0→0.3 nM). Subsequently, the level of gene silencing induced by each concentration is examined and siRNA that exhibit hyperfunctional potency (i.e., those that induce 80% silencing or greater at, e.g., picomolar concentrations) are identified. In a second study, the longevity profiles of siRNA having high (>−10) SMARTSCORES™, or siRNA rankings and greater than 80% silencing are examined. In one non-limiting example of how this is achieved, siRNA are introduced into a test cell line and the levels of RNAi are measured over an extended period of time (e.g., 24-168 hrs). siRNAs that exhibit strong RNA interference patterns (i.e., >80% interference) for periods of time greater than, e.g., 120 hours, are thus identified. Studies similar to those described above can be performed on any and all of the >10⁶ siRNA included in this document to further define the most functional molecule for any given gene. Molecules possessing one or both properties (extended longevity and heightened potency) are labeled “hyperfunctional siRNA,” and earmarked as candidates for future therapeutic studies.

While the example(s) given above describe one means by which hyperfunctional siRNA can be isolated, neither the assays themselves nor the selection parameters used are rigid and can vary with each family of siRNA. Families of siRNA include siRNAs directed against a single gene, or directed against a related family of genes.

The highest quality siRNA achievable for any given gene may vary considerably. Thus, for example, in the case of one gene (gene X), rigorous studies such as those described above may enable the identification of an siRNA that, at picomolar concentrations, induces 99⁺% silencing for a period of 10 days. Yet identical studies of a second gene (gene Y) may yield an siRNA that at high nanomolar concentrations (e.g., 100 nM) induces only 75% silencing for a period of 2 days. Both molecules represent the very optimum siRNA for their respective gene targets and therefore are designated “hyperfunctional.” Yet due to a variety of factors including but not limited to target concentration, siRNA stability, cell type, off-target interference, and others, equivalent levels of potency and longevity are not achievable. Thus, for these reasons, the parameters described in the before mentioned assays can vary. While the initial screen selected siRNA that had SMARTSCORES™ above −10 and a gene silencing capability of greater than 80%, selections that have stronger (or weaker) parameters can be implemented. Similarly, in the subsequent studies designed to identify molecules with high potency and longevity, the desired cutoff criteria (i.e., the lowest concentration that induces a desirable level of interference, or the longest period of time that interference can be observed) can vary. The experimentation subsequent to application of the rational criteria of this application is significantly reduced where one is trying to obtain a suitable hyperfunctional siRNA for, for example, therapeutic use. When, for example, the additional experimentation of the type described herein is applied by one skilled in the art with this disclosure in hand, a hyperfunctional siRNA is readily identified.

The siRNA may be introduced into a cell by any method that is now known or that comes to be known and that from reading this disclosure, persons skilled in the art would determine would be useful in connection with the present invention in enabling siRNA to cross the cellular membrane. These methods include, but are not limited to, any manner of transfection, such as, for example, transfection employing DEAE-Dextran, calcium phosphate, cationic lipids/liposomes, micelles, manipulation of pressure, microinjection, electroporation, immunoporation, use of vectors such as viruses, plasmids, cosmids, bacteriophages, cell fusions, and coupling of the polynucleotides to specific conjugates or ligands such as antibodies, antigens, or receptors, passive introduction, adding moieties to the siRNA that facilitate its uptake, and the like.

Having described the invention with a degree of particularity, examples will now be provided. These examples are not intended to and should not be construed to limit the scope of the claims in any way.

EXAMPLES General Techniques and Nomenclatures

siRNA nomenclature. All siRNA duplexes are referred to by sense strand. The first nucleotide of the 5′-end of the sense strand is position 1, which corresponds to position 19 of the antisense strand for a 19-mer. In most cases, to compare results from different experiments, silencing was determined by measuring specific transcript mRNA levels or enzymatic activity associated with specific transcript levels, 24 hours post-transfection, with siRNA concentrations held constant at 100 nM. For all experiments, unless otherwise specified, transfection efficiency was ensured to be over 95%, and no detectable cellular toxicity was observed. The following system of nomenclature was used to compare and report siRNA-silencing functionality: “F” followed by the degree of minimal knockdown. For example, F50 signifies at least 50% knockdown, F80 means at least 80%, and so forth. For this study, all sub-F50 siRNAs were considered non-functional.

Cell culture and transfection. 96-well plates are coated with 50 μl of 50 mg/ml poly-L-lysine (Sigma) for 1 hr, and then washed 3× with distilled water before being dried for 20 min. HEK293 cells or HEK293Lucs or any other cell type of interest are released from their solid support by trypsinization, diluted to 3.5×10⁵ cells/ml, followed by the addition of 100 μL of cells/well. Plates are then incubated overnight at 37° C., 5% CO₂. Transfection procedures can vary widely depending on the cell type and transfection reagents. In one non-limiting example, a transfection mixture consisting of 2 mL Opti-MEM 1 (Gibco-BRL), 80 μl Lipofectamine 2000 (Invitrogen), 15 μL SUPERNasin at 20 U/μl (Ambion), and 1.5 μl of reporter gene plasmid at 1 μg/μl is prepared in 5-ml polystyrene round bottom tubes. One hundred μl of transfection reagent is then combined with 100 μl of siRNAs in polystyrene deep-well titer plates (Beckman) and incubated for 20 to 30 min at room temperature. Five hundred and fifty microliters of Opti-MEM is then added to each well to bring the final siRNA concentration to 100 nM. Plates are then sealed with parafilm and mixed. Media is removed from HEK293 cells and replaced with 95 μl of transfection mixture. Cells are incubated overnight at 37° C., 5% CO₂.

Quantification of gene knockdown. A variety of quantification procedures can be used to measure the level of silencing induced by siRNA or siRNA pools. In one non-limiting example: to measure mRNA levels 24 hrs post-transfection, QuantiGene branched-DNA (bDNA) kits (Bayer) (Wang, et al, Regulation of insulin preRNA splicing by glucose. Proc. Natl. Acad. Sci. USA 1997, 94:4360.) are used according to manufacturer instructions. To measure luciferase activity, media is removed from HEK293 cells 24 hrs post-transfection, and 50 μl of Steady-GLO reagent (Promega) is added. After 5 minutes, plates are analyzed on a plate reader.

Example I Sequences Used to Develop the Algorithm

Anti-Firefly and anti-Cyclophilin siRNAs panels (FIG. 5 a, b) sorted according to using Formula VIII predicted values. All siRNAs scoring more than 0 (formula VIII) and more then 20 (formula IX) are fully functional. All ninety sequences for each gene (and DBI) appear below in Table III. TABLE III Cyclo 1 SEQ. ID 0032 GUUCCAAAAACAGUGGAUA Cyclo 2 SEQ. ID 0033 UCCAAAAACAGUGGAUAAU Cyclo 3 SEQ. ID 0034 CAAAAACAGUGGAUAAUUU Cyclo 4 SEQ. ID 0035 AAAACAGUGGAUAAUUUUG Cyclo 5 SEQ. ID 0036 AACAGUGGAUAAUUUUGUG Cyclo 6 SEQ. ID 0037 CAGUGGAUAAUUUUGUGGC Cyclo 7 SEQ. ID 0038 GUGGAUAAUUUUGUGGCCU Cyclo 8 SEQ. ID 0039 GGAUAAUUUUGUGGCCUUA Cyclo 9 SEQ. ID 0040 AUAAUUUUGUGGCCUUAGC Cyclo 10 SEQ. ID 0041 AAUUUUGUGGCCUUAGCUA Cyclo 11 SEQ. ID 0042 UUUUGUGGCCUUAGCUACA Cyclo 12 SEQ. ID 0043 UUGUGGCCUUAGCUACAGG Cyclo 13 SEQ. ID 0044 GUGGCCUUAGCUACAGGAG Cyclo 14 SEQ. ID 0045 GGCCUUAGCUACAGGAGAG Cyclo 15 SEQ. ID 0046 CCUUAGCUACAGGAGAGAA Cyclo 16 SEQ. ID 0047 UUAGCUACAGGAGAGAAAG Cyclo 17 SEQ. ID 0048 AGCUACAGGAGAGAAAGGA Cyclo 18 SEQ. ID 0049 CUACAGGAGAGAAAGGAUU Cyclo 19 SEQ. ID 0050 ACAGGAGAGAAAGGAUUUG Cyclo 20 SEQ. ID 0051 AGGAGAGAAAGGAUUUGGC Cyclo 21 SEQ. ID 0052 GAGAGAAAGGAUUUGGCUA Cyclo 22 SEQ. ID 0053 GAGAAAGGAUUUGGCUACA Cyclo 23 SEQ. ID 0054 GAAAGGAUUUGGCUACAAA Cyclo 24 SEQ. ID 0055 AAGGAUUUGGCUACAAAAA Cyclo 25 SEQ. ID 0056 GGAUUUGGCUACAAAAACA Cyclo 26 SEQ. ID 0057 AUUUGGCUACAAAAACAGC Cyclo 27 SEQ. ID 0058 UUGGCUACAAAAACAGCAA Cyclo 28 SEQ. ID 0059 GGCUACAAAAACAGCAAAU Cyclo 29 SEQ. ID 0060 CUACAAAAACAGCAAAUUC Cyclo 30 SEQ. ID 0061 ACAAAAACAGCAAAUUCCA Cyclo 31 SEQ. ID 0062 AAAAACAGCAAAUUCCAUC Cyclo 32 SEQ. ID 0063 AAACAGCAAAUUCCAUCGU Cyclo 33 SEQ. ID 0064 ACAGCAAAUUCCAUCGUGU Cyclo 34 SEQ. ID 0065 AGCAAAUUCCAUCGUGUAA Cyclo 35 SEQ. ID 0066 CAAAUUCCAUCGUGUAAUC Cyclo 36 SEQ. ID 0067 AAUUCCAUCGUGUAAUCAA Cyclo 37 SEQ. ID 0068 UUCCAUCGUGUAAUCAAGG Cyclo 38 SEQ. ID 0969 CCAUCGUGUAAUCAAGGAC Cyclo 39 SEQ. ID 0070 AUCGUGUAAUCAAGGACUU Cyclo 40 SEQ. ID 0071 CGUGUAAUCAAGGACUUCA Cyclo 41 SEQ. ID 0072 UGUAAUCAAGGACUUCAUG Cyclo 42 SEQ. ID 0073 UAAUCAAGGACUUCAUGAU Cyclo 43 SEQ. ID 0074 AUCAAGGACUUCAUGAUCC Cyclo 44 SEQ. ID 0075 CAAGGACUUCAUGAUCCAG Cyclo 45 SEQ. ID 0076 AGGACUUCAUGAUCCAGGG Cyclo 46 SEQ. ID 0077 GACUUCAUGAUCCAGGGCG Cyclo 47 SEQ. ID 0078 CUUCAUGAUCCAGGGCGGA Cyclo 48 SEQ. ID 0079 UCAUGAUCCAGGGCGGAGA Cyclo 49 SEQ. ID 0080 AUGAUCCAGGGCGGAGACU Cyclo 50 SEQ. ID 0081 GAUCCAGGGCGGAGACUUC Cyclo 51 SEQ. ID 0082 UCCAGGGCGCAGACUUCAC Cyclo 52 SEQ. ID 0083 CAGGGCGGAGACUUCACCA Cyclo 53 SEQ. ID 0084 GGGCGGACACUUCACCAGG Cyclo 54 SEQ. ID 0085 GCGGAGACUUCACCAGGGG Cyclo 55 SEQ. ID 0086 GGAGACUUCACCAGGGGAG Cyclo 56 SEQ. ID 0087 AGACUUCACCAGGGGAGAU Cyclo 57 SEQ. ID 0088 ACUUCACCAGGGGAGAUGG Cyclo 58 SEQ. ID 0089 UUCACCAGGGGAGAUGGCA Cyclo 59 SEQ. ID 0090 CACCAGGGGAGAUGGCACA Cyclo 60 SEQ. ID 0091 CCAGGGGAGAUGGCACAGG Cyclo 61 SEQ. ID 0092 AGGGGAGAUGGCACAGGAG Cyclo 62 SEQ. ID 0093 GGGAGAUGGCACAGGAGGA Cyclo 63 SEQ. ID 0094 GAGAUGGCACAGGAGGAAA Cyclo 64 SEQ. ID 0095 GAUGGCACAGGAGGAAAGA Cyclo 65 SEQ. ID 0096 UGGCACAGGAGGAAAGAGC Cyclo 66 SEQ. ID 0097 GCACAGGAGGAAAGAGCAU Cyclo 67 SEQ. ID 0098 ACAGGAGGAAAGAGCAUCU Cyclo 68 SEQ. ID 0099 AGGAGGAAAGAGCAUCUAC Cyclo 69 SEQ. ID 0100 GAGGAAAGAGCAUCUACGG Cyclo 70 SEQ. ID 0101 GGAAAGAGCAUCUACGGUG Cyclo 71 SEQ. ID 0102 AAAGAGCAUCUACGGUGAG Cyclo 72 SEQ. ID 0103 AGAGCAUCUACGGUGAGCG Cyclo 73 SEQ. ID 0104 AGCAUCUACGGUGAGCGCU Cyclo 74 SEQ. ID 0105 CAUCUACGGUGAGCGCUUC Cyclo 75 SEQ. ID 0106 UCUACGGUGAGCGCUUCCC Cyclo 76 SEQ. ID 0107 UACGGUGAGCGCUUCCCCG Cyclo 77 SEQ. ID 0108 CGGUGAGCGCUUCCCCGAU Cyclo 78 SEQ. ID 0109 GUGAGCGCUUCCCCGAUGA Cyclo 79 SEQ. ID 0110 GAGCGCUUCCCCGAUGAGA Cyclo 80 SEQ. ID 0111 GCGCUUCCCCGAUGAGAAC Cyclo 81 SEQ. ID 0112 GCUUCCCCGAUGAGAACUU Cyclo 82 SEQ. ID 0113 UUCCCCGAUGAGAACUUCA Cyclo 83 SEQ. ID 0114 CCCCGAUGAGAACUUCAAA Cyclo 84 SEQ. ID 0115 CCGAUGAGAACUUCAAACU Cyclo 85 SEQ. ID 0116 GAUGAGAACUUCAAACUGA Cyclo 86 SEQ. ID 0117 UGAGAACUUCAAACUGAAG Cyclo 87 SEQ. ID 0118 AGAACUUCAAACUGAAGCA Cyclo 88 SEQ. ID 0119 AACUUCAAACUGAAGCACU Cyclo 89 SEQ. ID 0120 CUUCAAACUGAAGCACUAC Cyclo 90 SEQ. ID 0121 UCAAACUGAAGCACUACGG DB 1 SEQ. ID 0122 ACGGGCAAGGCCAAGUGGG DB 2 SEQ. ID 0123 CGGGCAAGGCCAAGUGGGA DB 3 SEQ. ID 0124 GGGCAAGGCCAAGUGGGAU DB 4 SEQ. ID 0125 GGCAAGGCCAAGUGGGAUG DB 5 SEQ. ID 0126 GCAAGGCCAAGUGGGAUGC DB 6 SEQ. ID 0127 CAAGGCCAAGUGGGAUGCC DB 7 SEQ. ID 0128 AAGGCCAAGUGGGAUGCCU DB 8 SEQ. ID 0129 AGGCCAAGUGGGAUGCCUG DB 9 SEQ. ID 0130 GGCCAAGUGGGAUGCCUGG DB 10 SEQ. ID 0131 GCCAAGUGGGAUGCCUGGA DB 11 SEQ. ID 0132 CCAAGUGGGAUGCCUGGAA DB 12 SEQ. ID 0133 CAAGUGGGAUGCCUGGAAU DB 13 SEQ. ID 0134 AAGUGGGAUGCCUGGAAUG DB 14 SEQ. ID 0135 AGUGGGAUGCCUGGAAUGA DB 15 SEQ. ID 0136 GUGGGAUGCCUGGAAUGAG DB 16 SEQ. ID 0137 UGGGAUGCCUGGAAUGAGC DB 17 SEQ. ID 0138 GGGAUGCCUGGAAUGAGCU DB 18 SEQ. ID 0139 GGAUGCCUGGAAUGAGCUG DB 19 SEQ. ID 0140 GAUGCCUGGAAUGAGCUCA DB 20 SEQ. ID 0141 AUGCCUGGAAUGAGCUGAA DB 21 SEQ. ID 0142 UGCCUGGAAUGAGCUGAAA DB 22 SEQ. ID 0143 GCCUGGAAUGAGCUGAAAG DB 23 SEQ. ID 0144 CCUGGAAUGAGCUGAAAGG DB 24 SEQ. ID 0145 CUGGAAUGAGCUGAAAGGG DB 25 SEQ. ID 0146 UGGAAUGAGCUGAAAGGGA DB 26 SEQ. ID 0147 GGAAUGAGCUGAAAGGGAC DB 27 SEQ. ID 0148 GAAUGAGCUGAAAGGGACU DB 28 SEQ. ID 0149 AAUGAGCUGAAAGGGACUU DB 29 SEQ. ID 0150 AUGAGCUGAAAGGGACUUC DB 30 SEQ. ID 0151 UGAGCUGAAAGGGACUUCC DB 31 SEQ. ID 0152 GAGCUGAAAGGGACUUCCA DB 32 SEQ. ID 0153 AGCUGAAAGGGACUUCCAA DB 33 SEQ. ID 0154 GCUGAAAGGGACUUCCAAG DB 34 SEQ. ID 0155 CUGAAAGGGACUUCCAAGG DB 35 SEQ. ID 0156 UGAAAGGGACUUCCAAGGA DB 36 SEQ. ID 0157 GAAAGGGACUUCCAAGGAA DB 37 SEQ. ID 0158 AAAGGGACUUCCAAGGAAG DB 38 SEQ. ID 0159 AAGGGACUUCCAAGGAAGA DB 39 SEQ. ID 0160 AGGGACUUCCAAGGAAGAU DB 40 SEQ. ID 0161 GGGACUUCCAAGGAAGAUG DB 41 SEQ. ID 0162 GGACUUCCAAGGAAGAUGC DB 42 SEQ. ID 0163 GACUUCCAAGGAAGAUGCC DB 43 SEQ. ID 0164 ACUUCCAAGGAAGAUGCCA DB 44 SEQ. ID 0165 CUUCCAAGGAAGAUGCCAU DB 45 SEQ. ID 0166 UUCCAAGGAAGAUGCCAUG DB 46 SEQ. ID 0167 UCCAAGGAAGAUGCCAUGA DB 47 SEQ. ID 0168 CCAAGGAAGAUGCCAUGAA DB 48 SEQ. ID 0169 CAAGGAAGAUGCCAUGAAA DB 49 SEQ. ID 0170 AAGGAAGAUGCCAUGAAAG DB 50 SEQ. ID 0171 AGGAAGAUGCCAUGAAAGC DB 51 SEQ. ID 0172 GGAAGAUGCCAUGAAAGCU DB 52 SEQ. ID 0173 GAAGAUGCCAUGAAAGCUU DB 53 SEQ. ID 0174 AAGAUGCCAUGAAAGCUUA DB 54 SEQ. ID 0175 AGAUGCCAUGAAAGCUUAC DB 55 SEQ. ID 0176 GAUGCCAUGAAAGCUUACA DB 56 SEQ. ID 0177 AUGCCAUGAAAGCUUACAU DB 57 SEQ. ID 0178 UGCCAUGAAAGCUUACAUC DB 58 SEQ. ID 0179 GCCAUGAAAGCUUACAUCA DB 59 SEQ. ID 0180 CCAUGAAAGCUUACAUCAA DB 60 SEQ. ID 0181 CAUGAAAGCUUACAUCAAC DB 61 SEQ. ID 0182 AUGAAAGCUUACAUCAACA DB 62 SEQ. ID 0183 UGAAAGCUUACAUCAACAA DB 63 SEQ. ID 0184 GAAAGCUUACAUCAACAAA DB 64 SEQ. ID 0185 AAAGCUUACAUCAACAAAG DB 65 SEQ. ID 0186 AAGCUUACAUCAACAAAGU DB 66 SEQ. ID 0187 AGCUUACAUCAACAAAGUA DB 67 SEQ. ID 0188 GCUUACAUCAACAAAGUAG DB 68 SEQ. ID 0189 CUUACAUCAACAAAGUAGA DB 69 SEQ. ID 0190 UUACAUCAACAAAGUAGAA DB 70 SEQ. ID 0191 UACAUCAACAAAGUAGAAG DB 71 SEQ. ID 0192 ACAUCAACAAAGUAGAAGA DB 72 SEQ. ID 0193 CAUCAACAAAGUAGAAGAG DB 73 SEQ. ID 0194 AUCAACAAAGUAGAAGAGC DB 74 SEQ. ID 0195 UCAACAAAGUAGAAGAGCU DB 75 SEQ. ID 0196 CAACAAAGUAGAAGAGCUA DB 76 SEQ. ID 0197 AACAAAGUAGAAGAGCUAA DB 77 SEQ. ID 0198 ACAAAGUAGAAGAGCUAAA DB 78 SEQ. ID 0199 CAAAGUAGAAGAGCUAAAG DB 79 SEQ. ID 0200 AAAGUAGAAGAGCUAAAGA DB 80 SEQ. ID 0201 AAGUAGAAGAGCUAAAGAA DB 81 SEQ. ID 0202 AGUAGAAGAGCUAAAGAAA DB 82 SEQ. ID 0203 GUAGAAGAGCUAAAGAAAA DB 83 SEQ. ID 0204 UAGAAGAGCUAAAGAAAAA DB 84 SEQ. ID 0205 AGAAGAGCUAAAGAAAAAA DB 85 SEQ. ID 0206 GAAGAGCUAAAGAAAAAAU DB 86 SEQ. ID 0207 AAGAGCUAAAGAAAAAAUA DB 87 SEQ. ID 0208 AGAGCUAAAGAAAAAAUAC DB 88 SEQ. ID 0209 GAGCUAAAGAAAAAAUACG DB 89 SEQ. ID 0210 AGCUAAAGAAAAAAUACGG DB 90 SEQ. ID 0211 GCUAAAGAAAAAAUACGGG Luc 1 SEQ. ID 0212 AUCCUCAUAAAGGCCAAGA Luc 2 SEQ. ID 0213 AGAUCCUCAUAAAGGCCAA Luc 3 SEQ. ID 0214 AGAGAUCCUCAUAAAGGCC Luc 4 SEQ. ID 0215 AGAGAGAUCCUCAUAAAGG Luc 5 SEQ. ID 0216 UCAGAGAGAUCCUCAUAAA Luc 6 SEQ. ID 0217 AAUCAGAGAGAUCCUCAUA Luc 7 SEQ. ID 0218 AAAAUCAGAGAGAUCCUCA Luc 8 SEQ. ID 0219 GAAAAAUCAGAGAGAUCCU Luc 9 SEQ. ID 0220 AAGAAAAAUCAGAGAGAUC Luc 10 SEQ. ID 0221 GCAAGAAAAAUCAGAGAGA Luc 11 SEQ. ID 0222 ACGCAAGAAAAAUCAGAGA Luc 12 SEQ. ID 0223 CGACGCAAGAAAAAUCAGA Luc 13 SEQ. ID 0224 CUCGACGCAAGAAAAAUCA Luc 14 SEQ. ID 0225 AACUCGACGCAAGAAAAAU Luc 15 SEQ. ID 0226 AAAACUCGACGCAAGAAAA Luc 16 SEQ. ID 0227 GGAAAACUCGACGCAAGAA Luc 17 SEQ. ID 0228 CCGGAAAACUCGACGCAAG Luc 18 SEQ. ID 0229 UACCGGAAAACUCGACGCA Luc 19 SEQ. ID 0230 CUUACCGGAAAACUCGACG Luc 20 SEQ. ID 0231 GUCUUACCGGAAAACUCGA Luc 21 SEQ. ID 0232 AGGUCUUACCGGAAAACUC Luc 22 SEQ. ID 0233 AAAGGUCUUACCGGAAAAC Luc 23 SEQ. ID 0234 CGAAAGGUCUUACCGGAAA Luc 24 SEQ. ID 0235 ACCGAAAGGUCUUACCGGA Luc 25 SEQ. ID 0236 GUACCGAAAGGUCUUACCG Luc 26 SEQ. ID 0237 AAGUACCGAAAGGUCUUAC Luc 27 SEQ. ID 0238 CGAAGUACCGAAAGGUCUU Luc 28 SEQ. ID 0239 GACGAAGUACCGAAAGGUC Luc 29 SEQ. ID 0240 UGGACGAAGUACCGAAAGG Luc 30 SEQ. ID 0241 UGUGGACGAAGUACCGAAA Luc 31 SEQ. ID 0242 UUUGUGGACGAAGUACCGA Luc 32 SEQ. ID 0243 UGUUUGUGGACGAAGUACC Luc 33 SEQ. ID 0244 UGUGUUUGUGGACGAAGUA Luc 34 SEQ. ID 0245 GUUGUGUUUGUGGACGAAG Luc 35 SEQ. ID 0246 GAGUUGUGUUUGUGGACGA Luc 36 SEQ. ID 0247 AGGAGUUGUGUUUGUGGAC Luc 37 SEQ. ID 0248 GGAGGAGUUGUGUUUGUGG Luc 38 SEQ. ID 0249 GCGGAGGAGUUGUGUUUGU Luc 39 SEQ. ID 0250 GCGCGGAGGAGUUGUGUUU Luc 40 SEQ. ID 0251 UUGCGCGGAGGAGUUGUGU Luc 41 SEQ. ID 0252 AGUUGCGCGGAGGAGUUGU Luc 42 SEQ. ID 0253 AAAGUUGCGCGGAGGAGUU Luc 43 SEQ. ID 0254 AAAAAGUUGCGCGGAGGAG Luc 44 SEQ. ID 0255 CGAAAAAGUUGCGCGGAGG Luc 45 SEQ. ID 0256 CGCGAAAAAGUUGCGCGGA Luc 46 SEQ. ID 0257 ACCGCGAAAAAGUUGCGCG Luc 47 SEQ. ID 0258 CAACCGCGAAAAAGUUGCG Luc 48 SEQ. ID 0259 AACAACCGCGAAAAAGUUG Luc 49 SEQ. ID 0260 GUAACAACCGCGAAAAAGU Luc 50 SEQ. ID 0261 AAGUAACAACCGCGAAAAA Luc 51 SEQ. ID 0262 UCAAGUAACAACCGCGAAA Luc 52 SEQ. ID 0263 AGUCAAGUAACAACCGCGA Luc 53 SEQ. ID 0264 CCAGUCAAGUAACAACCGC Luc 54 SEQ. ID 0265 CGCCAGUCAAGUAACAACC Luc 55 SEQ. ID 0266 GUCGCCAGUCAAGUAACAA Luc 56 SEQ. ID 0267 ACGUCGCCAGUCAAGUAAC Luc 57 SEQ. ID 0268 UUACGUCGCCAGUCAAGUA Luc 58 SEQ. ID 0269 GAUUACGUCGCCAGUCAAG Luc 59 SEQ. ID 0270 UGGAUUACGUCGCCAGUCA Luc 60 SEQ. ID 0271 CGUGGAUUACGUCGCCAGU Luc 61 SEQ. ID 0272 AUCGUGGAUUACGUCGCCA Luc 62 SEQ. ID 0273 AGAUCGUGGAUUACGUCGC Luc 63 SEQ. ID 0274 AGAGAUCGUGGAUUACGUC Luc 64 SEQ. ID 0275 AAAGAGAUCGUGGAUUACG Luc 65 SEQ. ID 0276 AAAAAGAGAUCGUGGAUUA Luc 66 SEQ. ID 0277 GGAAAAAGAGAUCGUGGAU Luc 67 SEQ. ID 0278 ACGGAAAAAGAGAUCGUGG Luc 68 SEQ. ID 0279 UGACGGAAAAAGAGAUCGU Luc 69 SEQ. ID 0280 GAUGACGGAAAAAGAGAUC Luc 70 SEQ. ID 0281 ACGAUGACGGAAAAAGAGA Luc 71 SEQ. ID 0282 AGACGAUGACGGAAAAAGA Luc 72 SEQ. ID 0283 AAAGACGAUGACGGAAAAA Luc 73 SEQ. ID 0284 GGAAAGACGAUGACGGAAA Luc 74 SEQ. ID 0285 ACGGAAAGACGAUGACGGA Luc 75 SEQ. ID 0286 GCACGGAAAGACGAUGACG Luc 76 SEQ. ID 0287 GAGCACGGAAAGACGAUGA Luc 77 SEQ. ID 0288 UGGAGCACGGAAAGACGAU Luc 78 SEQ. ID 0289 UUUGGAGCACGGAAAGACG Luc 79 SEQ. ID 0290 GUUUUGGAGCACGGAAAGA Luc 80 SEQ. ID 0291 UUGUUUUGGAGCACGGAAA Luc 81 SEQ. ID 0292 UGUUGUUUUGGAGCACGGA Luc 82 SEQ. ID 0293 GUUGUUGUUUUGGAGCACG Luc 83 SEQ. ID 0294 CCGUUGUUGUUUUGGAGCA Luc 84 SEQ. ID 0295 CGCCGUUGUUGUUUUGGAG Luc 85 SEQ. ID 0296 GCCGCCGUUGUUGUUUUGG Luc 86 SEQ. ID 0297 CCGCCGCCGUUGUUGUUUU Luc 87 SEQ. ID 0298 UCCCGCCGCCGUUGUUGUU Luc 88 SEQ. ID 0299 CUUCCCGCCGCCGUUGUUG Luc 89 SEQ. ID 0300 AACUUCCCGCCGCCGUUGU Luc 90 SEQ. ID 0301 UGAACUUCCCGCCGCCGUU

Example II Validation of the Algorithm Using DBI, Luciferase, PLK, EGFR, and SEAP

The algorithm (Formula VIII) identified siRNAs for five genes, human DBI, firefly luciferase (fLuc), renilla luciferase (rLuc), human PLK, and human secreted alkaline phosphatase (SEAP). Four individual siRNAs were selected on the basis of their SMARTSCORES™ derived by analysis of their sequence using Formula VIII (all of the siRNAs would be selected with Formula IX as well) and analyzed for their ability to silence their targets expression. In addition to the scoring, a BLAST search was conducted for each siRNA. To minimize the potential for off-target silencing effects, only those target sequences with more than three mismatches against un-related sequences were selected. Semizarov, et al. (2003) Specificity of short interfering RNA determined through gene expression signatures, Proc. Natl. Acad. Sci. USA, 100:6347. These duplexes were analyzed individually and in pools of 4 and compared with several siRNAs that were randomly selected. The functionality was measured as a percentage of targeted gene knockdown as compared to controls. All siRNAs were transfected as described by the methods above at 100 nM concentration into HEK293 using Lipofectamine 2000. The level of the targeted gene expression was evaluated by B-DNA as described above and normalized to the non-specific control. FIG. 10 shows that the siRNAs selected by the algorithm disclosed herein were significantly more potent than randomly selected siRNAs. The algorithm increased the chances of identifying an F50 siRNA from 48% to 91%, and an F80 siRNA from 13% to 57%. In addition, pools of SMART siRNA silence the selected target better than randomly selected pools (see FIG. 10F).

Example III Validation of the Algorithm Using Genes Involved in Clathrin-Dependent Endocytosis

Components of clathrin-mediated endocytosis pathway are key to modulating intracellular signaling and play important roles in disease. Chromosomal rearrangements that result in fusion transcripts between the Mixed-Lineage Leukemia gene (MLL) and CALM (clathrin assembly lymphoid myeloid leukemia gene) are believed to play a role in leukemogenesis. Similarly, disruptions in Rab7 and Rab9, as well as HIP1 (Huntingtin-interacting protein), genes that are believed to be involved in endocytosis, are potentially responsible for ailments resulting in lipid storage, and neuronal diseases, respectively. For these reasons, siRNA directed against clathrin and other genes involved in the clathrin-mediated endocytotic pathway are potentially important research and therapeutic tools.

siRNAs directed against genes involved in the clathrin-mediated endocytosis pathways were selected using Formula VIII. The targeted genes were clathrin heavy chain (CHC, accession # NM_(—)004859), clathrin light chain A (CLCa. NM_(—)001833), clathrin light chain B (CLCb, NM_(—)001834), CALM (U45976), β2 subunit of AP-2 (β2, NM_(—)001282), Eps15 (NM_(—)001981), Eps15R (NM_(—)021235), dynamin II (DYNII, NM_(—)004945), Rab5a (BC001267), Rab5b (NM_(—)002868), Rab5c (AF141304), and EEA.1 (XM_(—)018197).

For each gene, four siRNAs duplexes with the highest scores were selected and a BLAST search was conducted for each of them using the Human EST database. In order to minimize the potential for off-target silencing effects, only those sequences with more than three mismatches against un-related sequences were used. All duplexes were synthesized at Dharmacon, Inc. as 21-mers with 3′-UU overhangs using a modified method of 2′-ACE chemistry, Scaringe (2000) Advanced 5′-silyl-2′-orthoester approach to RNA oligonucleotide synthesis, Methods Enzymol. 317:3, and the antisense strand was chemically phosphorylated to insure maximized activity.

HeLa cells were grown in Dulbecco's modified Eagle's medium (DMEM) containing 10% fetal bovine serum, antibiotics and glutamine. siRNA duplexes were resuspended in 1×siRNA Universal buffer (Dharmacon, Inc.) to 20 μM prior to transfection. HeLa cells in 12-well plates were transfected twice with 4 μl of 20 μM siRNA duplex in 3 μl Lipofectamine 2000 reagent (Invitrogen, Carlsbad, Calif., USA) at 24-hour intervals. For the transfections in which 2 or 3 siRNA duplexes were included, the amount of each duplex was decreased, so that the total amount was the same as in transfections with single siRNAs. Cells were plated into normal culture medium 12 hours prior to experiments, and protein levels were measured 2 or 4 days after the first transfection.

Equal amounts of lysates were resolved by electrophoresis, blotted, and stained with the antibody specific to targeted protein, as well as antibodies specific to unrelated proteins, PP1 phosphatase and Tsg101 (not shown). The cells were lysed in Triton X-100/glycerol solubilization buffer as described previously. Tebar, Bohlander, & Sorkin (1999) Clathrin Assembly Lymphoid Myeloid Leukemia (CALM) Protein: Localization in Endocytic-coated Pits, Interactions with Clathrin, and the Impact of Overexpression on Clathrin-mediated Traffic, Mol. Biol. Cell, 10:2687. Cell lysates were electrophoresed, transferred to nitrocellulose membranes, and Western blotting was performed with several antibodies followed by detection using enhanced chemiluminescence system (Pierce, Inc). Several x-ray films were analyzed to determine the linear range of the chemiluminescence signals, and the quantifications were performed using densitometry and AlphaImager v5.5 software (Alpha Innotech Corporation). In experiments with Eps15R-targeted siRNAs, cell lysates were subjected to immunoprecipitation with Ab860, and Eps15R was detected in immunoprecipitates by Western blotting as described above.

The antibodies to assess the levels of each protein by Western blot were obtained from the following sources: monoclonal antibody to clathrin heavy chain (TD.1) was obtained from American Type Culture Collection (Rockville, Md., USA); polyclonal antibody to dynamin II was obtained from Affinity Bioreagents, Inc. (Golden, Colo., USA); monoclonal antibodies to EEA.1 and Rab5a were purchased from BD Transduction Laboratories (Los Angeles, Calif., USA); the monoclonal antibody to Tsg101 was purchased from Santa Cruz Biotechnology, Inc. (Santa Cruz, Calif., USA); the monoclonal antibody to GFP was from ZYMED Laboratories Inc. (South San Francisco, Calif., USA); the rabbit polyclonal antibodies Ab32 specific to α-adaptins and Ab20 to CALM were described previously (Sorkin et al. (1995) Stoichiometric Interaction of the Epidermal Growth Factor Receptor with the Clathrin-associated Protein Complex AP-2, J. Biol. Chem., 270:619), the polyclonal antibodies to clathrin light chains A and B were kindly provided by Dr. F. Brodsky (UCSF); monoclonal antibodies to PP1 (BD Transduction Laboratories) and α-Actinin (Chemicon) were kindly provided by Dr. M. Dell'Acqua (University of Colorado); Eps15 Ab577 and Eps15R Ab860 were kindly provided by Dr. P. P. Di Fiore (European Cancer Institute).

FIG. 11 demonstrates the in vivo functionality of 48 individual siRNAs, selected using Formula VIII (most of them will meet the criteria incorporated by Formula IX as well) targeting 12 genes. Various cell lines were transfected with siRNA duplexes (Dup1-4) or pools of siRNA duplexes (Pool), and the cells were lysed 3 days after transfection with the exception of CALM (2 days) and β2 (4 days).

Note a β1-adaptin band (part of AP-1 Golgi adaptor complex) that runs slightly slower than β2 adaptin. CALM has two splice variants, 66 and 72 kD. The full-length Eps15R (a doublet of ˜130 kD) and several truncated spliced forms of ˜100 kD and ˜70 kD were detected in Eps15R immunoprecipitates (shown by arrows). The cells were lysed 3 days after transfection. Equal amounts of lysates were resolved by electrophoresis and blotted with the antibody specific to a targeted protein (GFP antibody for YFP fusion proteins) and the antibody specific to unrelated proteins PP1 phosphatase or α-actinin, and TSG101. The amount of protein in each specific band was normalized to the amount of non-specific proteins in each lane of the gel. Nearly all of them appear to be functional, which establishes that Formula VIII and IX can be used to predict siRNAs' functionality in general in a genome wide manner.

To generate the fusion of yellow fluorescent protein (YFP) with Rab5b or Rab5c (YFP-Rab5b or YFP-Rab5c), a DNA fragment encoding the full-length human Rab5b or Rab5c was obtained by PCR using Pfu polymerase (Stratagene) with a SacI restriction site introduced into the 5′ end and a KpnI site into the 3′ end and cloned into pEYFP-C1 vector (CLONTECH, Palo Alto, Calif., USA). GFP-CALM and YFP-Rab5a were described previously (Tebar, Bohlander, & Sorkin (1999) Clathrin Assembly Lymphoid Myeloid Leukemia (CALM) Protein: Localization in Endocytic-coated Pits, Interactions with Clathrin, and the Impact of Overexpression on Clathrin-mediated Traffic, Mol. Biol. Cell 10:2687).

Example IV Validation of the Algorithm Using Eg5, GADPH, ATE1, MEK2, MEK1, QB, Lamina/C, C-MYC, Human Cyclophilin, and Mouse Cyclophilin

A number of genes have been identified as playing potentially important roles in disease etiology. Expression profiles of normal and diseased kidneys has implicated Edg5 in immunoglobulin A neuropathy, a common renal glomerular disease. Myc1, MEK1/2 and other related kinases have been associated with one or more cancers, while lamins have been implicated in muscular dystrophy and other diseases. For these reasons, siRNA directed against the genes encoding these classes of molecules would be important research and therapeutic tools.

FIG. 12 illustrates four siRNAs targeting 10 different genes (Table V for sequence and accession number information) that were selected according to the Formula VIII and assayed as individuals and pools in HEK293 cells. The level of siRNA induced silencing was measured using the B-DNA assay. These studies demonstrated that thirty-six out of the forty individual SMART-selected siRNA tested are functional (90%) and all 10 pools are fully functional.

Example V Validation of the Algorithm Using Bcl2

Bcl-2 is a ˜25 kD, 205-239 amino acid, anti-apoptotic protein that contains considerable homology with other members of the BCL family including BCLX, MCL1, BAX, BAD, and BIK. The protein exists in at least two forms (Bcl2a, which has a hydrophobic tail for membrane anchorage, and Bcl2b, which lacks the hydrophobic tail) and is predominantly localized to the mitochondrial membrane. While Bcl2 expression is widely distributed, particular interest has focused on the expression of this molecule in B and T cells. Bcl2 expression is down-regulated in normal germinal center B cells yet in a high percentage of follicular lymphomas, Bcl2 expression has been observed to be elevated. Cytological studies have identified a common translocation ((14;18)(q32;q32)) amongst a high percentage (>70%) of these lymphomas. This genetic lesion places the Bcl2 gene in juxtaposition to immunoglobulin heavy chain gene (IgH) encoding sequences and is believed to enforce inappropriate levels of gene expression, and resistance to programmed cell death in the follicle center B cells. In other cases, hypomethylation of the Bcl2 promoter leads to enhanced expression and again, inhibition of apoptosis. In addition to cancer, dysregulated expression of Bcl-2 has been correlated with multiple sclerosis and various neurological diseases.

The correlation between Bcl-2 translocation and cancer makes this gene an attractive target for RNAi. Identification of siRNA directed against the bcl2 transcript (or Bcl2-IgH fusions) would further our understanding Bcl2 gene function and possibly provide a future therapeutic agent to battle diseases that result from altered expression or function of this gene.

In Silico Identification of Functional siRNA

To identify functional and hyperfunctional siRNA against the Bcl2 gene, the sequence for Bcl-2 was downloaded from the NCBI Unigene database and analyzed using the Formula VIII algorithm. As a result of these procedures, both the sequence and SMARTSCORES™, or siRNA rankings of the Bcl2 siRNA were obtained and ranked according to their functionality. Subsequently, these sequences were BLAST'ed (database) to insure that the selected sequences were specific and contained minimal overlap with unrelated genes. The SMARTSCORES™, or siRNA rankings for the top 10 Bcl-2 siRNA are identified in FIG. 13.

In Vivo Testing of Bcl-2 SiRNA

Bcl-2 siRNAs having the top ten SMARTSCORES™, or siRNA rankings were selected and tested in a functional assay to determine silencing efficiency. To accomplish this, each of the ten duplexes were synthesized using 2′-O-ACE chemistry and transfected at 100 nM concentrations into cells. Twenty-four hours later assays were performed on cell extracts to assess the degree of target silencing. Controls used in these experiments included mock transfected cells, and cells that were transfected with a non-specific siRNA duplex.

The results of these experiments are presented below (and in FIG. 14) and show that all ten of the selected siRNA induce 80% or better silencing of the Bcl2 message at 100 nM concentrations. These data verify that the algorithm successfully identified functional Bcl2 siRNA and provide a set of functional agents that can be used in experimental and therapeutic environments. siRNA 1 GGGAGAUAGUGAUGAAGUA SEQ. ID NO. 302 siRNA 2 GAAGUACAUCCAUUAUAAG SEQ. ID NO. 303 siRNA 3 GUACGACAACCGGGAGAUA SEQ. ID NO. 304 siRNA 4 AGAUAGUGAUGAAGUACAU SEQ. ID NO. 305 siRNA 5 UGAAGACUCUGCUCAGUUU SEQ. ID NO. 306 siRNA 6 GCAUGCGGCCUCUGUUUGA SEQ. ID NO. 307 siRNA 7 UGCGGCCUCUGUUUGAUUU SEQ. ID NO. 308 siRNA 8 GAGAUAGUGAUGAAGUACA SEQ. ID NO. 309 siRNA 9 GGAGAUAGUGAUGAAGUAC SEQ. ID NO. 310 siRNA 10 GAAGACUCUGCUCAGUUUG SEQ. ID NO. 311 Bcl2 siRNA: Sense Strand, 5′→3′

Example VI Sequences Selected by the Algorithm

Sequences of the siRNAs selected using Formulas (Algorithms) VIII and IX with their corresponding ranking, which have been evaluated for the silencing activity in vivo in the present study (Formula VIII and IX, respectively) are shown in Table V. It should be noted that the “t” residues in Table V, and elsewhere, when referring to siRNA, should be replaced by “u” residues. TABLE V FORMULA FORMULA GENE Name SEQ. ID No. FTLLSEQTENCE VIII IX CLTC NM_004859 0312 GAAAGAATCTGTAGAGAAA 76 94.2 CLTC NM_004859 0313 GCAATGAGCTGTTTGAAGA 65 39.9 CLTC NM_004859 0314 TGACAAAGGTGGATAAATT 57 38.2 CLTC NM_004859 0315 GGAAATGGATCTCTTTGAA 54 49.4 CLTA NM_001833 0316 GGAAAGTAATGGTCCAACA 22 55.5 CLTA NM_001833 0317 AGACAGTTATGCAGCTATT 4 22.9 CLTA NM_001833 0318 CCAATTCTCGGAAGCAAGA 1 17 CLTA NM_001833 0319 GAAAGTAATGGTCCAACAG −1 −13 CLTB NM_001834 0320 GCGCCAGAGTGAACAAGTA 17 57.5 CLTB NM_001834 0321 GAAGGTGGCCCAGCTATGT 15 −8.6 CLTB NM_001834 0322 GGAACCAGCGCCAGAGTGA 13 40.5 CLTB NM_001834 0323 GAGCGAGATTGCAGGCATA 20 61.7 CALM U45976 0324 GTTAGTATCTGATGACTTG 36 −34.6 CALM U45976 0325 GAAATGGAACCACTAAGAA 33 46.1 CALM U45976 0326 GGAAATGGAACCACTAAGA 30 61.2 CALM U45976 0327 CAACTACACTTTCCAATGC 28 6.8 EPS15 NM_001981 0328 CCACCAAGATTTCATGATA 48 25.2 EPS15 NM_001981 0329 GATCGGAACTCCAACAAGA 43 49.3 EPS15 NM_001981 0330 AAACGGAGCTACAGATTAT 39 11.5 EPS15 NM_001981 0331 CCACACAGCATTCTTGTAA 33 −23.6 EPS15R NM_021235 0332 GAAGTTACCTTGAGCAATC 48 33 EPS15R NM_021235 0333 GGACTTGGCCGATCCAGAA 27 33 EPS15R NM_021235 0334 GCACTTGGATCGAGATGAG 20 1.3 EPS15R NM_021235 0335 CAAAGACCAATTCGCGTTA 17 27.7 DNM2 NM_004945 0336 CCGAATCAATCGCATCTTC 6 −29.6 DNM2 NM_004945 0337 GACATGATCCTGCAGTTCA 5 −14 DNM2 NM_004945 0338 GAGCGAATCGTCACCACTT 5 24 DNM2 NM_004945 0339 CCTCCGAGCTGGCGTCTAC −4 −63.6 ARF6 AF93885 0340 TCACATGGTTAACCTCTAA 27 −21.1 ARF6 AF93885 0341 GATGAGGGACGCCATAATC 7 −38.4 ARF6 AF93885 0342 CCTCTAACTACAAATCTTA 4 16.9 ARF6 AF93885 0343 GGAAGGTGCTATCCAAAAT 4 11.5 RAB5A BC001267 0344 GCAAGCAAGTCCTAACATT 40 25.1 RAB5A BC001267 0345 GGAAGAGGAGTAGACCTTA 17 50.1 RAB5A BC001267 0346 AGGAATCAGTGTTGTAGTA 16 11.5 RAB5A BC001267 0347 GAAGAGGAGTAGACCTTAC 12 7 RAB5B NM_002868 0348 GAAAGTCAAGCCTGGTATT 14 18.1 RAB5B NM_002868 0349 AAAGTCAAGCCTGGTATTA 6 −17.8 RAB5B NM_002868 0350 GCTATGAACGTGAATGATC 3 −21.1 RAB5B NM_002868 0351 CAAGCCTGGTATTACGTTT −7 −37.5 RAB5C AF141304 0352 GGAACAAGATCTGTCAATT 38 51.9 RAB5C AF141304 0353 GCAATGAACGTGAACGAAA 29 43.7 RAB5C AF141304 0354 CAATGAACGTGAACGAAAT 18 43.3 RAB5C AF141304 0355 GGACAGGAGCGGTATCACA 6 18.2 EEA1 XM_018197 0356 AGACAGAGCTTGAGAATAA 67 64.1 EEA1 XM_018197 0357 GAGAAGATCTTTATGCAAA 60 48.7 EEA1 XM_018197 0358 GAAGAGAAATCAGCAGATA 58 45.7 EEA1 XM_018197 0359 GCAAGTAACTCAACTAACA 56 72.3 AP2B1 NM_001282 0360 GAGCTAATCTGCCACATTG 49 −12.4 AP2B1 NM_001282 0361 GCAGATGAGTTACTAGAAA 44 48.9 AP2B1 NM_001282 0362 CAACTTAATTGTCCAGAAA 41 28.2 AP2B1 NM_001282 0363 CAACACAGGATTCTGATAA 33 −5.8 PLK NM_005030 0364 AGATTGTGCCTAAGTCTCT −35 −3.4 PLK NM_005030 0365 ATGAAGATCTGGAGGTGAA 0 −4.3 PLK NM_005030 0366 TTTGAGACTTCTTGCCTAA −5 −27.7 PLK NM_005030 0367 AGATCACCCTCCTTAAATA 15 72.3 GAPDH NM_002046 0368 CAACGGATTTGGTCGTATT 27 −2.8 GAPDH NM_002046 0369 GAAATCCCATCACCATCTT 24 3.9 GAPDH NM_002046 0370 GACCTCAACTACATGGTTT 22 −22.9 GAPDH NM_002046 0371 TGGTTTACATGTTCCAATA 9 9.8 c-Myc 0372 GAAGAAATCGATGTTGTTT 31 −11.7 c-Myc 0373 ACACAAACTTGAACAGCTA 22 51.3 c-Myc 0374 GGAAGAAATCGATGTTGTT 18 26 c-Myc 0375 GAAACGACGAGAACAGTTG 18 −8.9 MAP2K1 NM_002755 0376 GCACATGGATGGAGGTTCT 26 16 MAP2K1 NM_002755 0377 GCAGAGAGAGCAGATTTGA 16 0.4 MAP2K1 NM_002755 0378 GAGGTTCTCTGGATCAAGT 14 15.5 MAP2K1 NM_002755 0379 GAGCAGATTTGAAGCAACT 14 18.5 MAP2K2 NM_030662 0380 CAAAGACGATGACTTCGAA 37 26.4 MAP2K2 NM_030662 0381 GATCAGCATTTGCATGGAA 24 −0.7 MAP2K2 NM_030662 0382 TCCAGGAGTTTGTCAATAA 17 −4.5 MAP2K2 NM_030662 0383 GGAAGCTGATCCACCTTGA 16 59.2 KNSL1 (EG5) NM_004523 0384 GCAGAAATCTAAGGATATA 53 35.8 KNSL1 (EG5) NM_004523 0385 CAACAAGGATGAAGTCTAT 50 18.3 KNSL1 (EGS) NM_004523 0386 CAGCAGAAATCTAAGGATA 41 32.7 KNSL1 (EG5) NM_004523 0387 CTAGATGGCTTTCTCAGTA 39 3.9 CyclophilinA NM_021130 0388 AGACAAGGTCCCAAAGACA −16 58.1 CyclophilinA NM_021130 0389 GGAATGGCAAGACCAGCAA −6 36 CyclophilinA NM_021130 0390 AGAATTATTCCAGGGTTTA −3 16.1 CyclophilinA NM_021130 0391 GCAGACAAGGTCCCAAAGA 8 8.9 LAMIN A/C NM_170707 0392 AGAAGCAGCTTCAGGATGA 31 38.8 LAMIN A/C NM_170707 0393 GAGCTTGACTTCCAGAAGA 33 22.4 LAMIN A/C NM_170707 0394 CCACCGAAGTTCACCCTAA 21 27.5 LAMIN A/C NM_170707 0395 GAGAAGAGCTCCTCCATCA 55 30.1 CyclophilinB M60857 0396 GAAAGAGCATCTACGGTGA 41 83.9 CyclophilinB M60857 0397 GAAAGGATTTGGCTACAAA 53 59.1 CyclophilinB M60857 0398 ACAGCAAATTCCATCGTGT −20 28.8 CyclophilinB M60857 0399 GGAAAGACTGTTCCAAAAA 2 27 DBI1 NM_020548 0400 CAACACGCCTCATCCTCTA 27 −7.6 DBI2 NM_020548 0401 CATGAAAGCTTACATCAAC 25 −30.8 DBI3 NM_020548 0402 AAGATGCCATGAAAGCTTA 17 22 DBI4 NM_020548 0403 GCACATACCGCCTGAGTCT 15 3.9 rLUC1 0404 GATCAAATCTGAAGAAGGA 57 49.2 rLUC2 0405 GCCAAGAAGTTTCCTAATA 50 13.7 rLUC3 0406 CAGCATATCTTGAACCATT 41 −2.2 rLUC4 0407 GAACAAAGGAAACGGATGA 39 29.2 SeAP1 NM_031313 0408 CGGAAACGGTCCAGGCTAT 6 26.9 SeAP2 NM_031313 0409 GCTTCGAGCAGACATGATA 4 −11.2 SeAP3 NM_031313 0410 CCTACACGGTCCTCCTATA 4 4.9 SeAP4 NM_031313 0411 GCCAAGAACCTCATCATCT 1 −9.9 fLUC1 0412 GATATGGGCTGAATACAAA 54 40.4 fLUC2 0413 GCACTCTGATTGACAAATA 47 54.7 fLUC3 0414 TGAAGTCTCTGATTAAGTA 46 34.5 fLUC4 0415 TCAGAGAGATCCTCATAAA 40 11.4 mCyclo_1 NM_008907 0416 GCAAGAAGATCACCATTTC 52 46.4 mCyclo_2 NM_008907 0417 GAGAGAAATTTGAGGATGA 36 70.7 mCyclo_3 NM_008907 0418 GAAAGGATTTGGCTATAAG 35 −1.5 mCyclo_4 NM_008907 0419 GAAAGAAGGCATGAACATT 27 10.3 BCL2_1 NM_000633 0420 GGGAGATAGTGATGAAGTA 21 72 BCL2_2 NM_000633 0421 GAAGTACATCCATTATAAG 1 3.3 BCL2_3 NM_000633 0422 GTACGACAACCGGGAGATA 1 35.9 BCL2_4 NM_000633 0423 AGATAGTGATGAAGTACAT −12 22.1 BCL2_5 NM_000633 0424 TGAAGACTCTGCTCAGTTT 36 19.1 BCL2_6 NM_000633 0425 GCATGCGGCCTCTGTTTGA 5 −9.7 QB1 NM_003365.1 0426 GCACACAGCUUACUACAUC 52 −4.8 QB2 NM_003365.1 0427 GAAAUGCCCUGGUAUCUCA 49 22.1 QB3 NM_003365.1 0428 GAAGGAACGUGAUGUGAUC 34 22.9 QB4 NM_003365.1 0429 GCACUACUCCUGUGUGUGA 28 20.4 ATE1-1 NM_007041 0430 GAACCCAGCUGGAGAACUU 45 15.5 ATE1-2 NM_007041 0431 GAUAUACAGUGUGAUCUUA 40 12.2 ATE1-3 NM_007041 0432 GUACUACGAUCCUGAUUAU 37 32.9 ATE1-4 NM_007041 0433 GUGCCGACCUUUACAAUUU 35 18.2 EGFR-1 NM_005228 0434 GAAGGAAACTGAATTCAAA 68 79.4 EGFR-1 NM_005228 0435 GGAAATATGTACTACGAAA 49 49.5 EGFR-1 NM_005228 0436 CCACAAAGCAGTGAATTTA 41 7.6 EGFR-1 NM_005228 0437 GTAACAAGCTCACGCAGTT 40 25.9

Many of the genes to which the described siRNA are directed play critical roles in disease etiology. For this reason, the siRNAs listed in the sequence listing may potentially act as therapeutic agents. A number of prophetic examples follow and should be understood in view of the siRNA that are identified in the sequence listing. To isolate these siRNAs, the appropriate message sequence for each gene is analyzed using one of the before mentioned formulas (preferably formula VIII) to identify potential siRNA targets. Subsequently these targets are BLAST'ed to eliminate homology with potential off-targets.

Example VII Evidence for the Benefits of Pooling

Evidence for the benefits of pooling have been demonstrated using the reporter gene, luciferase. Ninety siRNA duplexes were synthesized using Dharmacon proprietary ACE® chemistry against one of the standard reporter genes: firefly luciferase. The duplexes were designed to start two base pairs apart and to cover approximately 180 base pairs of the luciferase gene (see sequences in Table III). Subsequently, the siRNA duplexes were co-transfected with a luciferase expression reporter plasmid into HEK293 cells using standard transfection protocols and luciferase activity was assayed at 24 and 48 hours.

Transfection of individual siRNAs showed standard distribution of inhibitory effect. Some duplexes were active, while others were not. FIG. 15 represents a typical screen of ninety siRNA duplexes (SEQ. ID NO. 0032-0120) positioned two base pairs apart. As the figure suggests, the functionality of the siRNA duplex is determined more by a particular sequence of the oligonucleotide than by the relative oligonucleotide position within a gene or excessively sensitive part of the mRNA, which is important for traditional anti-sense technology.

When two continuous oligonucleotides were pooled together, a significant increase in gene silencing activity was observed (see FIGS. 16A and B). A gradual increase in efficacy and the frequency of pools functionality was observed when the number of siRNAs increased to 3 and 4 (FIGS. 16A, 16B, 17A, and 17B). Further, the relative positioning of the oligonucleotides within a pool did not determine whether a particular pool was functional (see FIGS. 18A and 18B, in which 100% of pools of oligonucleotides distanced by 2, 10 and 20 base pairs were functional).

However, relative positioning may nonetheless have an impact. An increased functionality may exist when the siRNA are positioned continuously head to toe (5′ end of one directly adjacent to the 3′ end of the others).

Additionally, siRNA pools that were tested performed at least as well as the best oligonucleotide in the pool, under the experimental conditions whose results are depicted in FIG. 19. Moreover, when previously identified non-functional and marginally (semi) functional siRNA duplexes were pooled together in groups of five at a time, a significant functional cooperative action was observed (see FIG. 20). In fact, pools of semi-active oligonucleotides were 5 to 25 times more functional than the most potent oligonucleotide in the pool. Therefore, pooling several siRNA duplexes together does not interfere with the functionality of the most potent siRNAs within a pool, and pooling provides an unexpected significant increase in overall functionality

Example VIII Additional Evidence of the Benefits of Pooling

Experiments were performed on the following genes: β-galactosidase, Renilla luciferase, and Secreted alkaline phosphatase, which demonstrates the benefits of pooling. (see FIGS. 21A, 21B and 21C). Individual and pools of siRNA (described in Figure legends 21A-C) were transfected into cells and tested for silencing efficiency. Approximately 50% of individual siRNAs designed to silence the above-specified genes were functional, while 100% of the pools that contain the same siRNA duplexes were functional.

Example IX Highly Functional siRNA

Pools of five siRNAs in which each two siRNAs overlap to 10-90% resulted in 98% functional entities (>80% silencing). Pools of siRNAs distributed throughout the mRNA that were evenly spaced, covering an approximate 20-2000 base pair range, were also functional. When the pools of siRNA were positioned continuously head to tail relative to in RNA sequences and mimicked the natural products of Dicer cleaved long double stranded RNA, 98% of the pools evidenced highly functional activity (>95% silencing).

Example X Human Cyclophilin B

Table III above lists the siRNA sequences for the human cyclophilin B protein. A particularly functional siRNA may be selected by applying these sequences to any of Formula I to VII above.

Alternatively, one could pool 2, 3, 4, 5 or more of these sequences to create a kit for silencing a gene. Preferably, within the kit there would be at least one sequence that has a relatively high predicted functionality when any of Formulas I-VII is applied.

Example XI Sample Pools of siRNAs and their Application to Human Disease

The genetic basis behind human disease is well documented and siRNA may be used as both research or diagnostic tools and therapeutic agents, either individually or in pools. Genes involved in signal transduction, the immune response, apoptosis, DNA repair, cell cycle control, and a variety of other physiological functions have clinical relevance and therapeutic agents that can modulate expression of these genes may alleviate some or all of the associated symptoms. In some instances, these genes can be described as a member of a family or class of genes and siRNA (randomly, conventionally, or rationally designed) can be directed against one or multiple members of the family to induce a desired result.

To identify rationally designed siRNA to each gene, the sequence was analyzed using Formula VIII or Formula X to identify rationally designed siRNA. To confirm the activity of these sequences, the siRNA are introduced into a cell type of choice (e.g., HeLa cells, HEK293 cells) and the levels of the appropriate message are analyzed using one of several art proven techniques. siRNA having heightened levels of potency can be identified by testing each of the before mentioned duplexes at increasingly limiting concentrations. Similarly, siRNA having increased levels of longevity can be identified by introducing each duplex into cells and testing functionality at 24, 48, 72, 96, 120, 144, 168, and 192 hours after transfection. Agents that induce >95% silencing at sub-nanomolar concentrations and/or induce functional levels of silencing for >96 hours are considered hyperfunctional.

Example XII Validation of Multigene Knockout Using Rab5 and Eps

Two or more genes having similar, overlapping functions often leads to genetic redundancy. Mutations that knockout only one of, e.g., a pair of such genes (also referred to as homologs) results in little or no phenotype due to the fact that the remaining intact gene is capable of fulfilling the role of the disrupted counterpart. To fully understand the function of such genes in cellular physiology, it is often necessary to knockout or knockdown both homologs simultaneously. Unfortunately, concomitant knockdown of two or more genes is frequently difficult to achieve in higher organisms (e.g., mice) thus it is necessary to introduce new technologies dissect gene function. One such approach to knocking down multiple genes simultaneously is by using siRNA. For example, FIG. 11 showed that rationally designed siRNA directed against a number of genes involved in the clathrin-mediated endocytosis pathway resulted in significant levels of protein reduction (e.g., >80%). To determine the effects of gene knockdown on clathrin-related endocytosis, internalization assays were performed using epidermal growth factor and transferrin. Specifically, mouse receptor-grade EGF (Collaborative Research Inc.) and iron-saturated human transferrin (Sigma) were iodinated as described previously (Jiang, X., Huang, F., Marusyk, A. & Sorkin, A. (2003) Mol Biol Cell 14, 858-70). HeLa cells grown in 12-well dishes were incubated with ¹²⁵I-EGF (1 ng/ml) or ¹²⁵I-transferrin (1 μg/ml) in binding medium (DM EM, 0.1% bovine serum albumin) at 37° C., and the ratio of internalized and surface radioactivity was determined during 5-min time course to calculate specific internalization rate constant k_(e) as described previously (Jiang, X et al.). The measurements of the uptakes of radiolabeled transferrin and EGF were performed using short time-course assays to avoid influence of the recycling on the uptake kinetics, and using low ligand concentration to avoid saturation of the clathrin-dependent pathway (for EGF Lund, K. A., Opresko, L. K., Strarbuck, C., Walsh, B. J. & Wiley, H. S. (1990) J. Biol. Chem. 265, 15713-13723).

The effects of knocking down Rab5a, 5b, 5c, Eps, or Eps 15R (individually) are shown in FIG. 22 and demonstrate that disruption of single genes has little or no effect on EGF or Tfn internalization. In contrast, simultaneous knock down of Rab5a, 5b, and 5c, or Eps and Eps 15R, leads to a distinct phenotype (note: total concentration of siRNA in these experiments remained constant with that in experiments in which a single siRNA was introduced, see FIG. 23). These experiments demonstrate the effectiveness of using rationally designed siRNA to knockdown multiple genes and validates the utility of these reagents to override genetic redundancy.

Example XIII Validation of Multigene Targeting Using G6PD, GAPDH, PLK, and UQC

Further demonstration of the ability to knock down expression of multiple genes using rationally designed siRNA was performed using pools of siRNA directed against four separate genes. To achieve this, siRNA were transfected into cells (total siRNA concentration of 100 nM) and assayed twenty-four hours later by B-DNA. Results shown in FIG. 24 show that pools of rationally designed molecules are capable of simultaneously silencing four different genes.

Example XIV Validation of Multigene Knockouts as Demonstrated by Gene Expression Profiling, a Prophetic Example

To further demonstrate the ability to concomitantly knockdown the expression of multiple gene targets, single siRNA or siRNA pools directed against a collection of genes (e.g., 4, 8, 16, or 23 different targets) are simultaneously transfected into cells and cultured for twenty-four hours. Subsequently, mRNA is harvested from treated (and untreated) cells and labeled with one of two fluorescent probes dyes (e.g., a red fluorescent probe for the treated cells, a green fluorescent probe for the control cells.). Equivalent amounts of labeled RNA from each sample is then mixed together and hybridized to sequences that have been linked to a solid support (e.g., a slide, “DNA CHIP”). Following hybridization, the slides are washed and analyzed to assess changes in the levels of target genes induced by siRNA.

Example XV Identifying Hyperfunctional siRNA

Identification of Hyperfunctional Bcl-2 siRNA

The ten rationally designed Bcl2 siRNA (identified in FIG. 13, 14) were tested to identify hyperpotent reagents. To accomplish this, each of the ten Bcl-2 siRNA were individually transfected into cells at a 300 pM (0.3 nM) concentrations. Twenty-four hours later, transcript levels were assessed by B-DNA assays and compared with relevant controls. As shown in FIG. 25, while the majority of Bcl-2 siRNA failed to induce functional levels of silencing at this concentration, siRNA 1 and 8 induced >80% silencing, and siRNA 6 exhibited greater than 90% silencing at this subnanomolar concentration.

By way of prophetic examples, similar assays could be performed with any of the groups of rationally designed genes described in the Examples. Thus for instance, rationally designed siRNA sequences directed against a gene of interest could be introduced into cells at increasingly limiting concentrations to determine whether any of the duplexes are hyperfunctional.

Example XVI Gene Silencing Prophetic Example

Below is an example of how one might transfect a cell.

Select a cell line. The selection of a cell line is usually determined by the desired application. The most important feature to RNAi is the level of expression of the gene of interest. It is highly recommended to use cell lines for which siRNA transfection conditions have been specified and validated.

Plate the cells. Approximately 24 hours prior to transfection, plate the cells at the appropriate density so that they will be approximately 70-90% confluent, or approximately 1×10⁵ cells/ml at the time of transfection. Cell densities that are too low may lead to toxicity due to excess exposure and uptake of transfection reagent-siRNA complexes. Cell densities that are too high may lead to low transfection efficiencies and little or no silencing. Incubate the cells overnight. Standard incubation conditions for mammalian cells are 37° C. in 5% CO₂. Other cell types, such as insect cells, require different temperatures and CO₂ concentrations that are readily ascertainable by persons skilled in the art. Use conditions appropriate for the cell type of interest.

siRNA re-suspension. Add 20 μl siRNA universal buffer to each siRNA to generate a final concentration of 50 μM.

siRNA-lipid complex formation. Use RNase-free solutions and tubes. Using the following table, Table XI: TABLE XI 96-WELL 24-WELL MIXTURE 1 (TRANSIT-TKO-PLASMID DILUTION MIXTURE) Opti-MEM 9.3 μl 46.5 μl TransIT-TKO (1 μg/μl) 0.5 μl 2.5 μl MIXTURE 1 10.0 μl 50.0 μl FINAL VOLUME MIXTURE 2 (SIRNA DILUTION MIXTURE) Opti-MEM 9.0 μl 45.0 μl siRNA (1 μM) 1.0 μl 5.0 μl MIXTURE 2 10.0 μl 50.0 μl FINAL VOLUME MIXTURE 3 (SIRNA-TRANSFECTION REAGENT MIXTURE) Mixture 1 10 μl 50 μl Mixture 2 10 μl 50 μl MIXTURE 3 20 μl 100 μl FINAL VOLUME Incubate 20 minutes at room temperature MIXTURE 4 (MEDIA-SIRNA/TRANSFECTION REAGENT MIXTURE) Mixture 3 20 μl 100 μl Complete media 80 μl 400 μl MIXTURE 4 100 μl 500 μl FINAL VOLUME Incubate 48 hours at 37° C.

Transfection. Create a Mixture 1 by combining the specified amounts of OPTI-MEM serum free media and transfection reagent in a sterile polystyrene tube. Create a Mixture 2 by combining specified amounts of each siRNA with OPTI-MEM media in sterile 1 ml tubes. Create a Mixture 3 by combining specified amounts of Mixture I and Mixture 2. Mix gently (do not vortex) and incubate at room temperature for 20 minutes. Create a Mixture 4 by combining specified amounts of Mixture 3 to complete media. Add appropriate volume to each cell culture well. Incubate cells with transfection reagent mixture for 24-72 hours at 37° C. This incubation time is flexible. The ratio of silencing will remain consistent at any point in the time period. Assay for gene silencing using an appropriate detection method such as RT-PCR, Western blot analysis, immunohistochemistry, phenotypic analysis, mass spectrometry, fluorescence, radioactive decay, or any other method that is now known or that comes to be known to persons skilled in the art and that from reading this disclosure would useful with the present invention. The optimal window for observing a knockdown phenotype is related to the mRNA turnover of the gene of interest, although 24-72 hours is standard. Final Volume reflects amount needed in each well for the desired cell culture format. When adjusting volumes for a Stock Mix, an additional 10% should be used to accommodate variability in pipetting, etc. Duplicate or triplicate assays should be carried out when possible.

Example XVII siRNAs that Target GCGR

siRNAs that target nucleotide sequences for GCGR(NCBI accession number NM_(—)000160) and having sequences generated in silico by the algorithms herein, are provided. In various embodiments, the siRNAs are rationally designed. In various embodiments, the siRNAs are functional or hyperfunctional. These siRNA that have been generated by the algorithms of the present invention include: siRNA Sense Sequence Sequence ID Number AAAUGUCCUCCAACAAUAA 438 AACAGAACCUUCGACAAGU 439 ACAAUAAAGAGCUCAAGUG 440 ACAGAACCUUCGACAAGUA 441 AGAAGGAGGUGGCCAAGAU 442 AGAUGGAUGGCGAGGAGAU 443 AGAUUGAGGUCCAGAAGGA 444 AGGAGGUGGCCAAGAUGUA 445 CAACAGAACCUUCGACAAG 446 CAGAACCUUCGACAAGUAU 447 CAUCCACGCGAAUCUGUUU 448 CAUCUUCGUCCGCAUCGUU 449 CCAAUACCACGGCCAACAU 450 CCACACAGACUACAAGUUC 451 CCACGGAGCUGGUGUGCAA 452 CCGCCAAGCUCUUCUUCGA 453 CCGCGGUGUUCAUGCAAUA 454 CCGCUCAGGUGAUGGACUU 455 CCUGGGCAGUGGUCAAGUG 456 CGAAUCUGUUUGCGUCCUU 457 CUGAUCAACUUCUUCAUCU 458 CUGGUGGCCUCCCUAGAUU 459 GAAAUGUCCUCCAACAAUA 460 GAACCUUCGACAAGUAUUC 461 GAAGGAGGUGGCCAAGAUG 462 GACCAGCAAUGACAACAUG 463 GAGCUGGUGUGCAACAGAA 464 GAUCAACUUCUUCAUCUUC 465 GAUGGAUGGCGAGGAGAUU 466 GCAAAGUGCUAUGGGAGGA 467 GCAACAGAACCUUCGAGAA 468 GCAAGGAGCUGCAGUUUGG 469 GCACCACACAGACUACAAG 470 GCAGCUUCCAGGUGAUGUA 471 GCAGUGGUCAAGUGUCUGU 472 GCGAGGAGAUUGAGGUCCA 473 GCGCCUGGGCAAAGUGCUA 474 GCUCCGUGCUGGUCAUUGA 475 GCUCUUCUUCGACCUCUUC 476 GCUGAGAGCCCCUUCUGAA 477 GCUGGUGGCCUCCCUAGAU 478 GCUGGUGGCUGUCCUCUAC 479 GGAAAUGUCCUCCAACAAU 480 GGACCAGCAAUGACAACAU 481 GGAGAUUGAGGUCCAGAAG 482 GGAGCUGGUGUGCAACAGA 483 GGAGGAGCGUACACACACA 484 GGAGGUGGCCAAGAUGUAC 485 GGGCAGUGGUCAAGUGUCU 486 GGUGAUGGACUUCCUGUUU 487 GUGAUGGACUUCCUGUUUG 488 GUGGCUGUCUGCGAGAUUG 489 UCAAGUGUCUGUUCGAGAA 490 UCCAGAAGGAGGUGGCCAA 491 UGAUCAACUUCUUCAUCUU 492 UGAUGGACUUCCUGUUUGA 493 UGAUGUACACAGUGGGCUA 494 UGCUAUGGGAGGAGCGGAA 495 UGGACUUCCUGUUUGAGAA 496 UGGCUGUCCUCUACUGCUU 497

Thus, consistent with Example XVII, the present invention provides an siRNA that targets a nucleotide sequence for GCGR, wherein the siRNA is selected from the group consisting of SEQ. ID NOs. 438-497.

In another embodiment, an siRNA is provided, said siRNA comprising a sense region and an antisense region, wherein said sense region and said antisense region are at least 90% complementary, said sense region and said antisense region together form a duplex region comprising 18-30 base pairs, and said sense region comprises a sequence that is at least 90% similar to a sequence selected from the group consisting of: SEQ. ID NOs 438-497.

In another embodiment, an siRNA is provided wherein the siRNA comprises a sense region and an antisense region, wherein said sense region and said antisense region are at least 90% complementary, said sense region and said antisense region together form a duplex region comprising 18-30 base pairs, and said sense region comprises a sequence that is identical to a contiguous stretch of at least 18 bases of a sequence selected from the group consisting of: SEQ. ID NOs 438-497.

In another embodiment, an siRNA is provided wherein the siRNA comprises a sense region and an antisense region, wherein said sense region and said antisense region are at least 90% complementary, said sense region and said antisense region together form a duplex region comprising 19-30 base pairs, and said sense region comprises a sequence that is identical to a contiguous stretch of at least 18 bases of a sequence selected from the group consisting of: SEQ. ID NOs 438-497.

In another embodiment, a pool of at least two siRNAs is provided, wherein said pool comprises a first siRNA and a second siRNA, said first siRNA comprises a duplex region of length 18-30 base pairs that has a first sense region that is at least 90% similar to 18 bases of a first sequence selected from the group consisting of: SEQ. ID NOs 438-497 and said second siRNA comprises a duplex region of length 18-30 base pairs that has a second sense region that is at least 90% similar to 18 bases of a second sequence selected from the group consisting of: SEQ. ID NOs 438-497 and wherein said first sense region and said second sense region are not identical.

In another embodiment, a pool of at least two siRNAs is provided, wherein said pool comprises a first siRNA and a second siRNA, said first siRNA comprises a duplex region of length 18-30 base pairs that has a first sense region that is identical to at least 18 bases of a sequence selected from the group consisting of: SEQ. ID NOs 438-497 and wherein the second siRNA comprises a second sense region that comprises a sequence that is identical to at least 18 bases of a sequence selected from the group consisting of: SEQ. ID NOs 438-497.

In another embodiment, a pool of at least two siRNAs is provided, wherein said pool comprises a first siRNA and a second siRNA, said first siRNA comprises a duplex region of length 19-30 base pairs and has a first sense region comprising a sequence that is at least 90% similar to a sequence selected from the group consisting of: SEQ. ID NOs 438-497, and said duplex of said second siRNA is 19-30 base pairs and comprises a second sense region that comprises a sequence that is at least 90% similar to a sequence selected from the group consisting of: SEQ. ID NOs 438-497.

In another embodiment, a pool of at least two siRNAs is provided, wherein said pool comprises a first siRNA and a second siRNA, said first siRNA comprises a duplex region of length 19-30 base pairs and has a first sense region comprising a sequence that is identical to at least 18 bases of a sequence selected the group consisting of: SEQ. ID NOs 438-497 and said duplex of said second siRNA is 19-30 base pairs and comprises a second sense region comprising a sequence that is identical to a sequence selected from the group consisting of: SEQ. ID NOs 438-497.

In each of the aforementioned embodiments, preferably the antisense region is at least 90% complementary to a contiguous stretch of bases of one of the NCBI sequences identified in Example XVII; each of the recited NCBI sequences is incorporated by reference as if set forth fully herein. In some embodiments, the antisense region is 100% complementary to a contiguous stretch of bases of one of the NCBI sequences identified in Example XVII.

Further, in some embodiments that are directed to siRNA duplexes in which the antisense region is 20-30 bases in length, preferably there is a stretch of 19 bases that is at least 90%, more preferably 100% complementary to the recited sequence id number and the entire antisense region is at least 90% and more preferably 100% complementary to a contiguous stretch of bases of one of the NCBI sequences identified in Example XVII.

While the invention has been described in connection with specific embodiments thereof, it will be understood that it is capable of further modifications and this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departure from the present disclosure as come within known or customary practice within the art to which the invention pertains and as may be applied to the essential features hereinbefore set forth and as follows in the scope of the appended claims. 

1. An siRNA comprising a sense region and an antisense region, wherein said sense region and said antisense region together form a duplex region, said antisense region and said sense region are each 18-30 nucleotides in length and said antisense region comprises a sequence that is at least 90% complementary to a sequence selected from the group consisting of SEQ. ID NOs. 438-497.
 2. An siRNA comprising a sense region and an antisense region, wherein said sense region and said antisense region together form a duplex region and said sense region and said antisense region are each 18-30 nucleotides in length, and said antisense region comprises a sequence that is 100% complementary to a contiguous stretch of at least 18 bases of a sequence selected from the group consisting of SEQ. ID NOs. 438-497.
 3. The siRNA of claim 2, wherein each of said antisense region and said sense region are 19-30 nucleotides in length, and said antisense region comprises a sequence that is 100% complementary to said sequence selected from the group consisting of: SEQ. ID NOs. 438-497.
 4. A pool of at least two siRNAs, wherein said pool comprises a first siRNA and a second siRNA, said first siRNA comprises a first antisense region and a first sense region that together form a first duplex region and each of said first antisense region and said first sense region are 18-30 nucleotides in length and said first antisense region is at least 90% complementary to 18 bases of a first sequence selected from the group consisting of: SEQ. ID NOs. 438-497 and said second siRNA comprises a second antisense region and a second sense region that together form a second duplex region and each of said second antisense region and said second sense region are 18-30 nucleotides in length and said second antisense region is at least 90% complementary to 18 bases of a second sequence selected from the group consisting of: SEQ. ID NOs. 438-497, wherein said first antisense region and said second antisense region are not identical.
 5. The pool of claim 4, wherein said first antisense region comprises a sequence that is 100% complementary to at least 18 bases of said first sequence, and said second antisense region comprises a sequence that is 100% complementary to at least 18 bases of said second sequence.
 6. The pool of claim 4, wherein said first siRNA is 19-30 nucleotides in length and said first antisense region comprises a sequence that is at least 90% complementary to said first sequence, and second siRNA is 19-30 nucleotides in length and said second antisense region comprises a sequence that is at least 90% complementary to said second sequence.
 7. The pool of claim 4, wherein said first antisense region is 19-30 nucleotides in length and said first antisense region comprises a sequence that is 100% complementary to at least 18 bases of said first sequence, and said second antisense region is 19-30 nucleotides in length and said second antisense region comprises a sequence that is 100% complementary to said second sequence.
 8. The siRNA of claim 1, wherein said antisense region and said sense region are each 19-25 nucleotides in length.
 9. The siRNA of claim 4, wherein said first antisense region, said first sense region, said second sense region and said second antisense region are each 19-25 nucleotides in length. 